Cover page

Eurema hecabe is alos popularly known as Common Grass Yellow butterﬂy often seen in Asia, Africa, and

Australia. It is a small, vibrant species often ﬂying low to the ground in grassy or scrubby areas.

Photo Credit: Vidyamol M .V, DCII Zoology, project student at airis4D from Christian College, Chenganoor

Managing Editor Chief Editor Editorial Board Correspondence

Ninan Sajeeth Philip Abraham Mulamoottil K Babu Joseph The Chief Editor

Ajit K Kembhavi airis4D

Geetha Paul Thelliyoor - 689544

Arun Kumar Aniyan India

Sindhu G

Journal Publisher Details

Publisher : airis4D, Thelliyoor 689544, India

Website : www.airis4d.com

Email : nsp@airis4d.com

Phone : +919497552476

Editorial

by Fr Dr Abraham Mulamoottil

airis4D, Vol.4, No.5, 2026

www.airis4d.com

Our journal is delighted to welcome Prof. K. Babu

Joseph, who launches a new monthly column titled

Vijnanam—a Sanskrit term meaning “knowledge.” A

former faculty member of CUSAT and current advisor to

the journal, Prof. Joseph is a distinguished physicist and

an accomplished writer of popular science, philosophy,

and poetry in both English and Malayalam. Through this

column, he will oﬀer rich interdisciplinary perspectives,

primarily focusing on science while also occasionally

engaging with other ﬁelds of thought.

The journal starts with the ﬁrst article, “Scientiﬁc

Knowledge in Evolution: Empiricism and Logical

Positivism” by K. Babu Joseph, which examines how

scientiﬁc knowledge develops through philosophical

inquiry. It begins by questioning the nature

of knowledge—traditionally seen as “justiﬁed true

belief”—and highlights its limitations and the need

for continuous validation. The discussion then focuses

on empiricism, which emphasises sensory experience as

the foundation of knowledge, while also acknowledging

Immanuel Kant’s view that reason plays a vital role.

The article further explores logical positivism, which

sought to deﬁne science through logic and veriﬁability,

but ultimately declined due to its inability to address

universal and abstract scientiﬁc statements, leading to

the emergence of more robust theories of scientiﬁc

progress.

The article “From Imagination to Innovation:

How Science Fiction Inspires Real Science” by Dr.

Arun Aniyan highlights how science ﬁction serves

as a powerful driver of real-world scientiﬁc and

technological progress. It explains that many modern

innovations—such as artiﬁcial intelligence, space

exploration, and biotechnology—were ﬁrst imagined

in sci-ﬁ narratives, which provide both inspiration and

ethical foresight. By visualising future possibilities,

simplifying complex ideas for the public, and oﬀering

both blueprints and warnings, science ﬁction motivates

scientists and engineers to turn imagination into reality,

ultimately shaping the direction of modern science and

innovation.

Blesson George’s article “Towards Eﬃcient

Learning in Neuromorphic Computing: A Hybrid

Probabilistic–Spike Approach” presents an innovative

framework to improve learning in neuromorphic

computing systems. It explains how traditional

spiking neural networks, though energy-eﬃcient, face

challenges in training due to their complex dynamics.

To address this, the author proposes a hybrid model

that combines probabilistic reasoning with spike-

based computation, enabling more adaptive, eﬃcient,

and scalable learning. By introducing feature-aware

connections, adaptive priors, and local learning rules,

the framework reduces computational complexity,

avoids the limitations of backpropagation, and supports

real-time, low-power applications, making it highly

suitable for advanced AI and neuromorphic hardware

systems.

The article “Can Machines Create, or Only

Rearrange Ideas?” by Jinsu Ann Mathew explores the

evolving debate on whether artiﬁcial intelligence can

truly be creative or simply recombine existing ideas. It

argues that while AI generates novel and often surprising

outputs by learning patterns and combining them in

new ways, this process closely resembles a fundamental

aspect of human creativity itself—recombination with

insight. However, the article highlights that human

creativity is also shaped by intention, emotion, and lived

experience, which AI lacks. Ultimately, it suggests that

AI does not just challenge the idea of machine creativity

but also deepens our understanding of what creativity

truly means, revealing it as a blend of recombination,

meaning, and human experience.

Abishek P.S.’s article “Plasma in the Interstellar

Medium” explains the crucial role of plasma in

shaping the structure and evolution of galaxies. It

describes the interstellar medium as a dynamic, multi-

phase environment where ionised plasma interacts

with magnetic ﬁelds, radiation, and cosmic events

like supernovae. The article highlights how plasma

governs key processes such as star formation, energy

transfer, turbulence, and chemical enrichment, while

also emphasising its dominance as the primary state of

visible matter in the universe. Through observational

evidence and discussion of challenges, it shows

that understanding interstellar plasma is essential for

explaining the life cycle of stars, the behaviour of

galaxies, and the broader evolution of the cosmos.

“Astronomy at Scale” by Ajay Vibhute discusses

how modern astronomy has transformed into a

data-driven science due to the massive growth of

observational datasets. It highlights how large-scale

data enables deeper insights into the universe, including

galaxy evolution and transient phenomena, while

requiring advanced computational tools like machine

learning, distributed computing, and automated

pipelines. The article also addresses challenges related

to data storage, access, movement, and the risk of

systematic errors, emphasising the need for robust,

transparent, and scalable methods. Overall, it shows

that integrating computation with astronomy is essential

for managing complexity and driving future scientiﬁc

discoveries.

The article “Black Hole Stories-26: Some Black

Hole Mergers From LIGO-Virgo-KAGRA Observing

Run O4” by Ajit Kembhavi presents an overview of

signiﬁcant black hole merger events detected during

the O4 observing run (2023–2025). It highlights

discoveries such as extremely massive and high-spin

mergers like GW231123, precise high signal-to-noise

detections like GW250104 that enable tests of general

relativity, and unusual systems including those formed

through hierarchical mergers or lying in mass gap

regions. The article emphasizes how improved detector

sensitivity has increased both the number and diversity

of observed GW detections, oﬀering deeper insights

into black hole formation, evolution, and fundamental

physics while also raising new questions about their

origins.

The article by Aromal P, “X-ray Astronomy:

Theory”, explains the origin and physics of

thermonuclear X-ray bursts in neutron star low-mass

X-ray binaries. It describes how accreted hydrogen and

helium on a neutron star’s surface undergo unstable

nuclear reactions, leading to rapid thermal runaway and

intense bursts of X-ray radiation. The article highlights

how diﬀerent accretion rates inﬂuence the nature of

these bursts, including processes like the CNO cycle

and rapid-proton (rp) process, and explains their impact

on the surrounding accretion disk and corona. It also

emphasises that studying these bursts provides valuable

insights into extreme physics, such as neutron star

structure, dense matter equations of state, and nuclear

processes under extreme conditions.

Aengela Grace Jacob’s article “Gene Knockout

Strategies – The Scientiﬁc Wrestle of Gene

Therapeutics” explains the principles and importance

of gene knockout techniques in genetic engineering

and cancer research. It describes how methods such

as homologous recombination, ZFNs, TALENs, and

especially CRISPR-Cas9 enable precise and permanent

disruption of genes to study their function and identify

therapeutic targets. The article highlights how these

strategies help in understanding disease mechanisms,

developing targeted cancer treatments like CAR T-cell

therapy, and discovering critical genetic vulnerabilities

through approaches such as CRISPR screening. It

also discusses emerging insights like isoform switching

in cancers, emphasising the potential of gene-based

precision medicine for more eﬀective and selective

treatments.

The article “The eDNA Metabarcoding Model:

Next-Generation Biodiversity Assessment” by

Geetha Paul explains how environmental DNA

iii

(eDNA) metabarcoding is revolutionising biodiversity

monitoring by detecting genetic traces that organisms

leave in their environment. It describes the scientiﬁc

workﬂow from DNA collection and sequencing

to bioinformatics analysis, highlighting tools like

universal primers and high-throughput sequencing

to identify entire ecosystems from a single sample.

The article emphasises the method’s advantages,

including high sensitivity, non-invasive sampling,

and eﬃciency in detecting rare or invasive species,

while also addressing challenges such as database

gaps and technical biases. Overall, it presents eDNA

metabarcoding as a transformative approach for

conservation, ecological research, and environmental

management.

News Desk

Photo courtesy: Prof. G. Ambika, his long term research collaborator.

Dr. K P Harikrishnan is no more

The airis4D family and the global scientiﬁc fraternity are deeply grieved by the sudden and tragic passing

of Dr. K. P. Harikrishnan, former faculty at the Department of Physics, Cochin College. He was a Research

Associate of IUCAA, Pune for more than 20 years and in this collaboration has published around 30 research

papers related to data driven dynamics of astrophysical sources.

Beyond his brilliant contributions as a researcher, ’Hari’ was a beacon of kindness—a soul whose trademark

was a gentle smile and words that healed. In a world of chaos, he remained an island of serenity, never once

known to be angry or unkind. We bow our heads in homage to his exceptional life and his restless scientiﬁc

curiosity. Rest in eternal peace, dear Hari. You will be missed beyond measure.

Contents

Editorial ii

I Vijnana 1

1 Scientiﬁc Knowledge in Evolution :

Empiricism and Logical Positivism 2

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Empiricism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Logical Positivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

II Artiﬁcial Intelligence and Machine Learning 4

1 From Imagination to Innovation: How Science Fiction Inspires Real Science 5

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2 The Power of the First Look . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 Fictional Inventions That Became Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 A Look at Modern Scientiﬁc Fields and Their Sci-Fi Roots . . . . . . . . . . . . . . . . . . . . . 6

1.5 How You Can Contribute to the Next Big Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Towards Eﬃcient Learning in Neuromorphic Computing:

A Hybrid Probabilistic–Spike Approach 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Proposed Hybrid Learning Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.4 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.5 Computational Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Can Machines Create, or Only Rearrange Ideas? 13

3.1 Why Creativity Often Looks Like Recombination . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 How AI Generates “New” Ideas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 Where AI Seems Creative — and Where It Still Falls Short . . . . . . . . . . . . . . . . . . . . . 15

3.4 Have We Misunderstood Creativity All Along? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

III Astronomy and Astrophysics 18

1 Plasma in the Interstellar Medium 19

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.2 Plasma in the Interstellar Medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

CONTENTS

1.3 Multi-phase Plasma Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4 Physical Processes in Interstellar Medium Plasma . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.5 Observational Evidences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.6 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.7 Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2 Astronomy at Scale 24

2.1 Growth of Astronomical Data Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2 Why Manual Analysis Failed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.3 Data-Intensive Astronomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.4 Storage, Access, and Data Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.5 Scientiﬁc Implications of Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3 Black Hole Stories-26

Some Black Hole Mergers From LIGO-Virgo-KAGRA Observing Run O4 27

3.1 The Fourth Observing Run O4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2 Compact Object Masses Found in the Four Observing Runs: . . . . . . . . . . . . . . . . . . . . 31

4 X-ray Astronomy: Theory 33

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Thermonuclear X-ray burst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

IV Biosciences 36

1 Gene Knockout Strategies – The Scientiﬁc Wrestle Of Gene Therapeutics 37

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

1.2 Fundamental Logic Of Knockout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

1.3 Traditional Homologous Recombination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

1.4 The Programmable Nucleases (ZFNs and TALENs) . . . . . . . . . . . . . . . . . . . . . . . . . 38

1.5 CRISPR-Cas9: The Genetic Revolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

1.6 Why We Need Gene Knockout? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

1.7 Assisting Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

1.8 The Great Genetic Shuﬄe: A Symphony of Survival in Esophageal Cancer . . . . . . . . . . . . 39

1.9 The Shifting Genetic Mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

1.10 The TTLL12 Gambit: Evading the Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . 40

1.11 The HM13 Maneuver: Stressing the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

1.12 A New Horizon for Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2 The eDNA Metabarcoding Model: Next-Generation Biodiversity Assessment - Part 2 41

2.1 Introduction: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.2 The Theoretical Framework: From Cells to Sequences . . . . . . . . . . . . . . . . . . . . . . . . 41

2.3 The Technical Pipeline: Field to Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.4 Strategic Advantages and the Biotic Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.5 Challenges: The Road to Standardisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

vii

Part I

Vijnana

Scientiﬁc Knowledge in Evolution :

Empiricism and Logical Positivism

by K. Babu Joseph

airis4D, Vol.4, No.5, 2026

www.airis4d.com

1.1 Introduction

Vijnana is the Sanskrit equivalent of knowledge

in English, which represents understanding developed

through experience and reasoning. It spans a wide

range of categories like facts, skills, and capacity of

independent thinking. Plato (1) quotes a Socratic

dialogue in which knowledge is tentatively deﬁned

as justiﬁed true belief. He knew that this deﬁnition is

insuﬃcient to capture cases like dreams, inspirations

and intuitions, all of which can sometimes be the

sources of knowledge. But, how can their genuinity be

justiﬁed? No way! The epithet ’true’ is also unclear. In

1963, the American philosopher Gettier (2) challenged

the conventional deﬁnition of knowledge by means of

counterexamples in which the beliefs become justiﬁed

by accident or coincidence but cannot be treated as

knowledge. The emerging consensus is that there is no

universally accepted deﬁnition of knowledge! We leave

this foundational terrain to philosophers of science for

further grinding.

Turning to science, some regard it as systematic

knowledge in any branch of learning, empirical

knowledge being only a part. Should science be

restricted to experiential knowledge , vast domains

of higher mathematics would be left out, a catastrophe

that should be avoided at all cost! In the present

article, we examine the implications of each of the

three words: justiﬁed, true. and belief, and proceed

to consider some major theories about how scientiﬁc

knowledge evolves. If empirical justiﬁcation alone is

available in a particular case, then it can be considered

true, postponing its theoretical justiﬁcation to the

future. Belief is a matter of psychology such that

sometimes even false things happen to be believed for

long periods. The geocentric theory of the universe is

a classic example of erroneous belief that gave rise to

anthropocentrism in thought and action. It is necessary

to check and recheck, from time to time the validity of

any knowledge. With these introductory remarks, we

turn to some important movements that took place in

the last century to characterise the growth pattern of

science.

1.2 Empiricism

That knowledge arises primarily from sensations or

sensory experience is the principal thesis of empiricism

(3). Ideas and reason are secondary things that develop

on the basis of sensations. This school was in existence

in India in the form of the Vaiseshika philosophy which

stressed perception and the Charvaka school’s trust

in sensory experience (4). Both Ayurveda and Greek

medicine followed the empirical approach in medicine.

Philosophers have tried to distinguish between diﬀerent

types of empiricism like concept- empiricism and

belief-empiricism. The diﬃculty with such a level of

abstraction is that it is the individual and not a common

experience that forms its basis. For the purposes of

understanding science, we will ignore such subtleties

and deﬁne empiricism to be the school that gives

primacy to experience in general. Immanuel Kant (5), a

1.3 Logical Positivism

great German philosopher of the 18th century, believed

that reason is above experience. The essential point he

wanted to argue was that experience must coherence

with reason. Kant posed the following questions: If

experience is the foundation of all knowledge, how is

pure math possible, and how is natural science possible?

In answering these, he developed his theory of synthetic

a priori knowledge, which precedes experience, and

therefore, must validate the latter.

1.3 Logical Positivism

Logical Positivism (6) also known as logical

empiricism or neopositivism, took birth in the early 20th

century among philosophers of science in Vienna under

the leadership of Moritz Schlick, and in Berlin under

the leadership of Hans Reichenbach. Both Karl Popper

and Wittgenstein used to unoﬃcially participate in the

group’s discussions. Ernst Mach suggested an empirical

origin of mathematical concepts. Logical positivists,

on the other hand, believed that mathematical truth

is the same as logical consistency. But it became

clear that logical , rules are ﬁxed only by convention.

There arose a classiﬁcation of sentences into two types:

analytic and synthetic. Analytic statements have no

empirical reference, but synthetic ones combine formal

and empirical aspects, and therefore, are suitable for

natural science. Here we give ann example for each.

All bachelors are unmarried ( analytic) . Politicians are

unselﬁsh servants of the country (synthetic). Besides

this classiﬁcation, the positivists proposed a veriﬁability

criterion too, which deﬁnes the meaning of a sentence

to be as given by the method of its veriﬁcation. Science

should, therefore, consist only of veriﬁable sentences.

The veriﬁability criterion obviously fails in the case

of universal statements like ” All metals expand when

heated”, because of the sheer impossibity of veriﬁcation

in all cases. We know that logical positivism is not

useful for evaluating the truth in a branch like cosmology

which deals not only with heavenly objects but also

with esoteric problems such as the beginning and end

( if any) of the universe. Logical Positivism declined,

and ﬁnally met with an ungraceful collapse when more

realistic theories such as Popper’s and Kuhn’s were put

forward .

References

1. Plato, Thaetetus, a Socratic dialogue

Edmund Gettier, ’ Is Justiﬁed True Belief

Knowledge ’, Analysis, 23, no. 6, 121 (1963)

Edward Craig, Editor,The Shorter Routledge

Encyclopaedia of Philosophy, (2005)

M. Hiriyanna, The Essentials of Indian

Philosophy, 1949, Reprint 2024

Immanuel Kant, The Critique of Pure Reason

(1781, 1787), The Critique of Practical Reason

(1788), and The Critique of the Power of

Judgement (1790) , or Edward Craig, ibid

6. Edward Craig, ibid

About the Author

Prof. K. Babu Joseph, who was formerly

the Vice Chancellor of Cochin University of Science

and Technology (CUSAT), is a well-known Physicist

and writer. Besides scientiﬁc journals, he has written

several books spanning a wide spectrum of topics from

popular science, philosophy and poetry in English and

Malayalam.

Part II

Artiﬁcial Intelligence and Machine Learning

From Imagination to Innovation: How Science

Fiction Inspires Real Science

by Arun Aniyan

airis4D, Vol.4, No.5, 2026

www.airis4d.com

1.1 Introduction

Have you ever stopped to truly consider the genesis

of a smartphone, the blueprint for a robot vacuum that

autonomously cleans your home, or the foundational

vision for space travel itself? It’s a fascinating pattern:

the seeds of today’s most revolutionary scientiﬁc and

technological achievements were often sown not within

the sterile conﬁnes of a research laboratory, but within

the boundless, vibrant narratives of a science ﬁction

book or projected onto a cinematic screen.

Science ﬁction, universally known as ”sci-ﬁ,”

transcends its reputation as mere escapism featuring

extraterrestrials and futuristic dystopias. It is, in fact,

a deeply profound and powerful engine for creative

inspiration and technological foresight. It functions as

an unconstrained playground for the human imagination,

granting us the liberty to meticulously explore the

”what if” scenarios of science and technology entirely

unburdened by the practical, physical, and ﬁnancial

limitations of current reality. This imaginative freedom

is crucial; it doesn’t just entertain, but actively fuels

the ambitions, determination, and innovative spirit of

scientists, engineers, inventors, and policymakers. By

consistently portraying the technologically impossible

as merely ”not yet possible,” science ﬁction provides a

critical long-term vision, catalyzing the eﬀort required

to make the most ambitious dreams—like interstellar

travel or sentient artiﬁcial intelligence—an eventual,

tangible reality. The genre eﬀectively functions as

a cultural precursor, laying the psychological and

conceptual groundwork long before the technology

can physically exist.

1.2 The Power of the First Look

Science ﬁction’s most signiﬁcant contribution is

its ability to visualize and popularize concepts before

they are technically feasible. By doing this, it achieves

several critical things:

1.2.1 It Provides a Goal to Strive For

Science ﬁction often presents a destination. For

example, the ﬁctional starship Enterprise from the

Star Trek series, with its ability to cross vast distances

instantaneously, established ”warp drive” as the ultimate

travel goal, even if the current technology is light-years

away. These grand, ambitious ideas inspire generations

of researchers to dedicate their lives to solving the

complex problems that stand in the way of achieving

these ﬁctional dreams.

1.2.2 It Makes Complex Ideas Accessible

Through compelling narratives, sci-ﬁ introduces

non-technical audiences to advanced concepts like

artiﬁcial intelligence, genetic engineering, and parallel

universes. This widespread familiarity can garner

public support and funding for research that might

otherwise seem too obscure or abstract.

1.4 A Look at Modern Scientiﬁc Fields and Their Sci-Fi Roots

1.2.3 It Oﬀers a Blueprint (or a Warning)

While not literal instructions, the detailed

descriptions of ﬁctional devices often give engineers

a starting point. More importantly, sci-ﬁ explores

the ethical and social consequences of technology.

Stories about robots turning against humans (like in

The Terminator) or gene-editing creating a two-tiered

society encourage real scientists to think deeply about

safety, ethics, and the long-term impact of their work.

1.3 Fictional Inventions That Became

Reality

The history of technology is littered with examples

of scientiﬁc breakthroughs that were ﬁrst envisioned in

science ﬁction. This is shown in Table 1.1

1.4 A Look at Modern Scientiﬁc

Fields and Their Sci-Fi Roots

From Imagination to Innovation: How Science

Fiction Inspires Real Science

Science ﬁction is not merely escapism; it serves as

a powerful and indispensable wellspring of ideas that

actively shapes the future by posing profound questions

and imagining revolutionary technologies. This

visionary role is evident in three major contemporary

ﬁelds where the line between ﬁction and reality

continues to blur.

1.4.1

Artiﬁcial Intelligence (AI) and Robotics:

Deﬁning the Intelligent Machine

Long before the development of commercial

products like Google’s Assistant, Amazon’s Alexa, or

advanced industrial automation, science ﬁction authors

were grappling with the complex philosophical and

engineering challenges of creating intelligent machines.

The foundational concepts of what AI could be—from

a helpful, empathetic automaton like C-3PO from Star

Wars to the menacing, self-aware computer HAL 9000

from 2001: A Space Odyssey—were meticulously

explored.

Pioneering authors like Isaac Asimov established

seminal frameworks, such as the famous Three Laws of

Robotics, which were intended to regulate the behavior

of complex machines and ensure human safety. These

ﬁctional laws laid the groundwork for contemporary

ethical discussions and research into AI alignment and

safe AI design. The ultimate measure of machine

intelligence, the ”Turing Test”—where a machine’s

ability to exhibit intelligent behavior indistinguishable

from a human’s is tested—was a concept extensively

popularized and dissected in these narratives. The core,

enduring questions ﬁrst posed in these stories—Can a

machine achieve genuine sentience? What are the

moral responsibilities of its creator? How do we

prevent unintended consequences?—remain central

drivers for researchers, ethicists, and policymakers

in the burgeoning ﬁeld of AI today.

1.4.2 Space Exploration and Propulsion:

Igniting the Cosmic Ambition

Humanity’s relentless ambition to break the

bounds of Earth and venture to Mars, establish

permanent space habitats, or explore exoplanets is a

direct and powerful inheritance from the works of early

20th-century sci-ﬁ authors. Luminaries such as H.G.

Wells, Jules Verne, and Robert Heinlein didn’t just write

about space travel; they meticulously envisioned the

necessary engineering and logistical solutions.

Ideas considered radical at the time—including

the concept of multi-stage chemical rockets (as detailed

by Arthur C. Clarke), the necessity of space stations

for long-duration missions (a common setting in many

Golden Age stories), and the possibility of sustained

human presence in extraterrestrial environments—were

normalized and popularized decades before the ﬁrst

actual rocket launch. This ﬁctional foresight served

as an inspirational blueprint. Space agencies globally,

most notably NASA and the European Space Agency

(ESA), have frequently and openly acknowledged

the critical, inspirational role that science ﬁction

plays. These narratives often serve as the ﬁrst spark,

encouraging countless young readers to pursue careers

in STEM (Science, Technology, Engineering, and

1.5 How You Can Contribute to the Next Big Idea

Fictional Concept Sci-Fi Origin Real-World Application

Submarine

Twenty Thousand Leagues Under the Sea

by Jules Verne (1870)

Modern military and

research submarines

Earbuds /

Hands-Free Comms

Fahrenheit 451 by Ray Bradbury (1953) Bluetooth earpieces and earbuds

Credit Cards/

Electronic Banking

Looking Backward by Edward Bellamy (1888) Modern debit and credit card systems

Robots

R.U.R. (Rossum’s Universal Robots)

by Karel

Capek (1920)

Industrial robots, robotic surgery,

and advanced AI

Communications

Satellites

”The Arthur C. Clarke Orbit” (1945)

Geostationary communication

satellites

Mathematics), ultimately supplying the next generation

of astronauts, rocket scientists, and aerospace engineers

necessary to turn ﬁctional dreams into concrete reality.

1.4.3 Biotechnology and Genetic Editing:

Re-engineering Life Itself

The ﬁeld of biotechnology, particularly the

revolutionary advancements in genetic editing, owes

a signiﬁcant debt to science ﬁction for exploring

its ultimate potential and inherent risks. Fiction

has long delved into the profound ethical and

societal implications of modifying the human form.

Narratives range from the utopian vision of eliminating

disease, engineering perfect, ”designer” children, and

signiﬁcantly extending the human lifespan, to dystopian

warnings of unintended biological mutations and a

future deﬁned by genetic stratiﬁcation.

Contemporary genetic editing tools, such as

CRISPR (Clustered Regularly Interspaced Short

Palindromic Repeats), represent the real-world

fulﬁllment of a capability ﬁrst imagined in ﬁction.

While the day-to-day reality of laboratory genetic work

is far more complex and incremental than a dramatic

sci-ﬁ plot, the genre provides the ultimate thought

experiment. It presents researchers, bioethicists, and

the public with the most extreme and compelling

scenarios—both the incredible beneﬁts of curing

inherited diseases and the potential pitfalls associated

with tampering with the fundamental blueprint of life.

Sci-ﬁ thus acts as a crucial ethical laboratory, preparing

society for technologies that can fundamentally change

what it means to be human.

1.5 How You Can Contribute to the

Next Big Idea

You don’t have to be a scientist to be inspired

by science ﬁction. The next wave of innovation often

comes from thinking outside the box, which is exactly

what sci-ﬁ encourages.

Read and Watch Critically: When you read or

watch science ﬁction, don’t just enjoy the story.

Ask yourself: What is the piece of technology

here? How does it work? What problems does it

solve? Could this really be built in the next ten

years?

Encourage Imagination: Support educational

programs that foster creative thinking and

storytelling alongside scientiﬁc study. The

combination of imagination and technical skill is

the formula for future breakthroughs.

Support Foundational Research: Remember

that every ﬁctional device, from invisibility

cloaks to instant teleportation, requires basic,

often unfunded, scientiﬁc research to even begin

to be realized.

Science ﬁction provides the visionary spark, the

audacious dream that challenges the status quo. It is

the compass pointing scientists toward the next great

frontier, reminding us that the only real limits are those

of our own imagination.

The next time you enjoy a piece of sci-ﬁ, remember

that you might just be looking at the world of tomorrow.

1.5 How You Can Contribute to the Next Big Idea

About the Author

Dr.Arun Aniyan is leading the R&D for

Artiﬁcial intelligence at DeepAlert Ltd,UK. He comes

from an academic background and has experience

in designing machine learning products for diﬀerent

domains. His major interest is knowledge representation

and computer vision.

Towards Eﬃcient Learning in Neuromorphic

Computing:

A Hybrid Probabilistic–Spike Approach

by Blesson George

airis4D, Vol.4, No.5, 2026

www.airis4d.com

2.1 Introduction

Neuromorphic computing has emerged as a

powerful paradigm aimed at replicating the eﬃciency

and adaptability of the human brain. Unlike

traditional computing systems, which rely on sequential

processing and separate memory and computation units,

neuromorphic systems operate using distributed, event-

driven architectures.

Spiking neural networks (SNNs) form the

computational backbone of neuromorphic systems.

These networks process information through discrete

spike events, enabling low-power and real-time

computation. However, despite their advantages,

training SNNs remains a challenging problem due to

their non-diﬀerentiable nature and temporal dynamics.

Existing approaches to learning in SNNs often rely

on biologically inspired rules such as Hebbian learning

and spike-timing dependent plasticity (STDP). While

these methods provide local learning capabilities, they

lack the ﬂexibility and eﬃciency of modern machine

learning techniques.

In this paper, we propose a hybrid probabilistic–

spike learning framework that integrates probabilistic

reasoning with spike-based neural computation. The

proposed model overcomes key limitations of existing

approaches by introducing feature-aware connections,

adaptive prior updating, and eﬃcient learning dynamics.

2.2 Background and Motivation

Biological neural systems learn through synaptic

plasticity, where connections between neurons are

strengthened or weakened based on activity. Hebbian

learning captures this principle through correlation-

based updates, while STDP introduces temporal

sensitivity by considering the timing of spikes.

Although these mechanisms are biologically

plausible, they are not suﬃcient for solving complex

computational tasks eﬃciently. On the other hand,

probabilistic models such as Bayesian learning provide

a strong mathematical framework for inference and

uncertainty handling but are not directly compatible

with spike-based computation.

This gap motivates the development of hybrid

approaches that combine the strengths of probabilistic

modeling and neuromorphic computation.

2.3 Proposed Hybrid Learning

Framework

2.3.1 Model Representation

Let the input feature vector be deﬁned as:

X = {x

, x

, . . . , x

} (2.1)

Each feature is connected to the output neuron

2.4 Proposed Algorithm

through a synaptic weight interpreted probabilistically:

= P (y∣x

) (2.2)

Unlike naive probabilistic models, the proposed

framework accounts for feature interactions by

introducing adaptive importance factors.

2.3.2 Feature Interaction Modeling

The conditional probability of the output is

expressed as:

P (y∣X) ∝

∏

i=1

P (y∣x

)

(2.3)

where

represents the importance of each feature

and is learned dynamically.

This formulation relaxes the independence

assumption and enables more expressive

representations.

2.3.3 Spike-Based Computation

The membrane potential of a neuron is computed

as:

V (t) =

∑

(t) (2.4)

A spike is generated when:

V (t) ≥ V

(2.5)

This event-driven mechanism ensures eﬃcient

computation.

2.3.4 Adaptive Prior Updating

The prior probability is updated incrementally as:

t+1

(y) = (1 − η)P

(y) + η ⋅

P (y∣X) (2.6)

where η is the learning rate.

This allows the system to adapt continuously to

new data.

2.3.5 Weight Learning Rule

The synaptic weights are updated using a

probabilistic learning rule:

new

= w

old

+ η ⋅ (x

y − w

old

) (2.7)

This update balances stability and adaptability.

2.4 Proposed Algorithm

The complete training procedure is outlined below:

Algorithm 1: Hybrid Probabilistic–Spike Learning

Initialize weights

and prior probabilities

P (y)

2. For each input sample X:

(a). Compute membrane potential:

V =

∑

(b). Generate spike if V ≥ V

(c). Estimate posterior probability P (y∣X)

(d). Update weights:

← w

+ η(x

y − w

)

(e). Update prior:

P (y) ← (1 − η)P (y) + ηP (y∣X)

3. Repeat until convergence

2.5 Computational Advantages

The proposed hybrid probabilistic–spike learning

framework oﬀers several computational advantages

that make it well suited for eﬃcient and scalable

intelligent systems. By combining probabilistic

reasoning with event-driven neural computation, the

framework achieves a balance between expressiveness

and eﬃciency.

2.5.1 Event-Driven Eﬃciency

Unlike conventional neural networks that perform

continuous computations, neuromorphic systems

operate in an event-driven manner. Computation occurs

only when spikes are generated. This signiﬁcantly

reduces unnecessary processing when inputs are

inactive, leading to lower energy consumption and

improved eﬃciency. Such behavior is particularly

advantageous for sparse and real-time data streams.

2.5.2 Reduced Computational Complexity

Traditional probabilistic models often

require either strong independence assumptions

or computationally expensive joint probability

2.6 Conclusion

estimation. The proposed framework avoids both

extremes by introducing feature-weighted probabilistic

contributions. By assigning adaptive importance

factors to features, the model captures relevant

interactions without requiring full joint distributions.

This results in a substantial reduction in computational

complexity while maintaining expressive power.

2.5.3 Parallel Processing Capability

Neuromorphic systems naturally support parallel

computation, as neurons operate independently and

simultaneously. In the proposed model, each feature

contributes independently to the neuron’s activation, and

weight updates are performed locally. This eliminates

the need for centralized computation and makes the

framework highly compatible with parallel architectures

such as GPUs and neuromorphic hardware.

2.5.4 Incremental and Online Learning

The framework supports continuous and

incremental learning through adaptive updates of both

synaptic weights and prior probabilities. Instead

of requiring batch training over large datasets, the

system updates its parameters on a per-sample basis.

This reduces memory requirements, shortens training

time, and enables real-time learning in dynamic

environments.

2.5.5 Avoidance of Backpropagation

Bottleneck

Conventional deep learning methods rely

on backpropagation, which requires global error

propagation and high computational cost. In contrast,

the proposed approach employs local update rules based

on spike activity and probabilistic adjustments. This

eliminates the need for global gradient computation,

making the model more eﬃcient and suitable for

hardware implementation.

2.5.6 Sparse Computation

Spike-based processing inherently leads to sparse

activity, as only a subset of neurons is active at any given

time. This sparsity reduces the number of computations

required and improves overall eﬃciency. Sparse

representations also contribute to better scalability in

large networks.

2.5.7 Hardware Compatibility

The proposed framework is well aligned with the

constraints of neuromorphic hardware. It supports

local memory usage, low-precision computation, and

event-driven processing. These characteristics make

it suitable for deployment in low-power devices,

embedded systems, and edge computing environments.

2.5.8 Scalability

Due to its reliance on local learning rules,

parallel processing, and reduced dependence on global

information, the framework scales eﬃciently to larger

systems. As network size increases, computational cost

grows in a manageable manner, making the approach

practical for real-world applications.

These applications beneﬁt from low power

consumption and eﬃcient learning.

2.6 Conclusion

This paper presented a hybrid probabilistic–spike

learning framework for neuromorphic computing

systems. By integrating probabilistic inference

with spike-based neural dynamics, the proposed

model addresses key limitations of existing learning

approaches. The framework introduces feature-aware

connections, adaptive priors, and eﬃcient learning rules,

enabling scalable and energy-eﬃcient computation.

Future work may focus on experimental validation,

hardware implementation, and integration with deep

learning systems to further enhance performance.

2.6 Conclusion

About the Author

Dr. Blesson George presently serves as

an Assistant Professor of Physics at CMS College

Kottayam, Kerala. His research pursuits encompass

the development of machine learning algorithms, along

with the utilization of machine learning techniques

across diverse domains.

Can Machines Create, or Only Rearrange

Ideas?

by Jinsu Ann Mathew

airis4D, Vol.4, No.5, 2026

www.airis4d.com

When an AI writes a poem, composes music,

designs an image, or suggests an unexpected solution to

a problem, the reaction is often a mixture of fascination

and discomfort. There is a sense that something

remarkable is happening—but also uncertainty about

how to interpret it. Are we witnessing creativity in a

genuine sense, or simply the output of a system that has

become extraordinarily skilled at combining patterns?

This question has moved from philosophy into

everyday conversation. Generative AI systems can

now produce stories, paintings, songs, code, and even

research ideas that sometimes appear original and

imaginative. A prompt as simple as “write a detective

story set on Mars” can produce something coherent and

surprising within seconds. For many, this feels creative.

However, others remain skeptical, arguing that these

systems are not inventing anything truly new—they are

only remixing what they have already seen.

At the heart of this debate lies a deeper question

about creativity itself. What do we actually mean when

we call something creative? Does creativity involve

conscious intention, imagination, and lived experience?

Or can it emerge whenever existing ideas are combined

in novel and meaningful ways?

Traditionally, creativity has often been treated

as one of the most distinctive human capacities. We

associate it with originality, inspiration, and the ability

to produce something that did not exist before. It

belongs to artists, scientists, writers, and inventors. It

feels tied to insight and imagination in a way that seems

diﬃcult to reduce to rules or algorithms.

Yet, when we look closely, even human creativity

is rarely created from nothing. New ideas often grow

out of old ones. Innovations emerge by connecting

concepts that were previously separate. A scientiﬁc

breakthrough may combine ideas from two ﬁelds. A

novel may reshape familiar themes into something fresh.

A piece of music may transform existing inﬂuences into

a new expression. In this sense, creativity often involves

not invention from emptiness, but recombination with

insight.

This is where AI makes the question so intriguing.

If creativity frequently involves recombining patterns

in unexpected ways, then what exactly separates human

creativity from what generative models do? Is AI

merely imitating creativity, or is it participating in one

of its fundamental mechanisms?

The question becomes even more interesting

because it does not only challenge our understanding of

machines—it challenges our understanding of ourselves.

Asking whether AI can be creative forces us to ask what

creativity actually is.

Is creativity something uniquely tied to

consciousness? Or could it, at least in part, emerge from

the surprising rearrangement of ideas? Exploring that

possibility may tell us as much about human imagination

as it does about artiﬁcial intelligence.

3.2 How AI Generates “New” Ideas

3.1 Why Creativity Often Looks Like

Recombination

We often think of creativity as the act of producing

something completely original—something that appears

almost out of nowhere. A brilliant scientiﬁc theory, a

timeless painting, a new musical style, or a powerful

story can feel like a sudden spark of invention. Because

of this, creativity is often associated with novelty in

its purest form, as though the creative mind generates

ideas from nothing.

But when we look more closely, creativity often

works diﬀerently. Many of the ideas we call original

are not created in isolation. They emerge by reshaping,

connecting, or extending ideas that already exist. What

appears revolutionary is often a new arrangement of

familiar pieces.

Scientiﬁc progress oﬀers many examples. Major

discoveries are frequently born when ideas from

diﬀerent domains are brought together in unexpected

ways. Breakthroughs often arise not from abandoning

existing knowledge, but from seeing connections others

had missed. Creativity here lies less in inventing entirely

new building blocks and more in combining known

ones in transformative ways.

Art follows a similar pattern. Writers draw

on myths, histories, and earlier literary traditions,

yet produce stories that feel original. Musicians

borrow rhythms, scales, or styles and transform them

into something new. Painters absorb inﬂuences but

reinterpret them through a diﬀerent vision. What

makes the work creative is not the absence of prior

inﬂuence, but the originality of how those inﬂuences

are reassembled.

Even everyday creativity often works through this

process. A clever metaphor joins two unrelated ideas.

A joke works because it connects expectations in an

unexpected way. A novel solution to a practical problem

often comes from applying an old idea in a new setting.

In this sense, creativity frequently involves

recombination, but not mere rearrangement. It is not

random mixing. It is the meaningful joining of existing

elements in ways that produce novelty.

A simple example makes this clearer. Imagine

combining the detective logic of Sherlock Holmes with

the futuristic world of science ﬁction. Neither idea is

new on its own, yet their combination may produce

an entirely fresh narrative world. Much creative work

operates in exactly this way.

This does not reduce creativity to mechanical

mixing. The value lies in which connections are made,

how they are transformed, and whether something

genuinely unexpected emerges. Recombination

becomes creative when it produces insight, surprise, or

meaning.

This perspective changes the debate about machine

creativity in an important way. If even human creativity

often depends on connecting and transforming existing

ideas, then recombination may not be the opposite of

creativity at all. It may be one of its central mechanisms.

And if that is true, then the question is no longer whether

creativity involves recombination. It becomes whether

machines can engage in that process in a way that

deserves to be called creative.

3.2 How AI Generates “New” Ideas

Once we recognize that creativity often involves

combining existing ideas in unexpected ways, the

discussion about AI becomes much more interesting.

The question is no longer whether machines start from

existing material—they clearly do, just as humans often

do. The real question is whether the way they recombine

ideas can lead to something genuinely new.

To understand this, it helps to look at how

generative AI produces output.

A common misconception is that AI simply stores

vast amounts of information and copies pieces of it

when prompted. But modern generative models do

not work like giant databases retrieving ready-made

answers. Instead, they learn patterns, relationships, and

structures from enormous amounts of data, and use

those learned patterns to generate new responses.

This is an important distinction. When asked to

write a poem about the moon in the style of classical

literature, the model is not pulling a hidden poem from

storage. It is generating one word at a time based on

3.3 Where AI Seems Creative — and Where It Still Falls Short

patterns it has learned about poetry, imagery, language,

rhythm, and style. What emerges is not a direct copy,

but a new construction shaped by those patterns.

Consider a prompt such as: Imagine a conversation

between a medieval philosopher and a modern robot

about consciousness.

There may be no exact example of this in the

model’s training data. Yet the system can generate

something plausible—even surprising—by combining

patterns associated with philosophy, dialogue, history,

and science ﬁction. This is where AI begins to look

creative. It can produce unusual combinations that may

not have existed before. It can blend concepts from

distant domains, generate analogies, suggest design

ideas, or oﬀer unexpected turns in a story. Sometimes

the result may even surprise the person who gave the

prompt.

That sense of surprise is important, because

surprise is often part of what we associate with

creativity.

Of course, one might argue that this is still just

recombination at scale. But perhaps scale changes what

recombination can do. When patterns become rich

enough and combinations become suﬃciently complex,

novelty can emerge in ways that begin to resemble

creative behavior.

A useful comparison is with music. A composer

works within patterns of harmony and rhythm, yet

can still create something original. Constraints do not

eliminate creativity; they often make it possible. In a

diﬀerent way, generative AI operates through learned

constraints and patterns, yet can still produce outputs

that feel new.

This does not necessarily mean machines possess

imagination in the human sense. But it does suggest

that novelty can arise from pattern generation in ways

that are more sophisticated than simple imitation. And

this complicates the old assumption that machines can

only repeat what they have seen. Sometimes they do

something closer to synthesis. And once synthesis

enters the picture, the line between recombination and

creativity begins to blur.

3.3 Where AI Seems Creative — and

Where It Still Falls Short

There are many situations where AI appears

creative.

For example, suppose you ask an AI to suggest a

logo for a coﬀee shop inspired by astronomy. It might

combine stars, coﬀee cups, and constellations in ways

you had not thought of. Or imagine asking it to write a

fairy tale set in a future where robots and humans live

together. It may produce a story that feels original and

imaginative.

In cases like these, AI can seem more than a

tool—it can feel like a creative partner. This is one

reason people increasingly use AI for brainstorming. A

writer may use it to generate plot ideas, an artist may

explore design variations, or a researcher may use it to

test unusual perspectives. Sometimes the value is not

in the ﬁnal output itself, but in the unexpected ideas it

helps trigger. That certainly looks like creativity. But

there is also a limit.

Consider a human poet writing about grief. The

poem may come from personal loss, memory, and

emotion. An AI can generate lines about grief, but

it has never felt loss. It can imitate the language of

emotion, but not the experience behind it. That seems

like an important diﬀerence.

The same applies to imagination. A human child

imagining a dragon kingdom may do so from curiosity,

play, and wonder. An AI can generate a description of

such a world, but it does not dream it in the same way.

This is why some people say AI can produce creative

outputs, but may not possess creativity itself.

A simple way to think about it is this: AI can

generate novel combinations Humans often create with

novel combinations plus intention and experience. The

ﬁrst may look creative. The second may be what we

usually call creativity. Still, even this distinction can

become blurry.

Suppose an AI-generated melody inspires a

musician to compose a new song. Or an AI-generated

concept helps an architect design a building. Was the AI

merely assisting, or did it contribute creatively? There

3.5 Conclusion

may not be a sharp boundary. Perhaps AI is not creative

in exactly the human sense, but it may participate in

part of the creative process—especially in generating

unexpected possibilities. And that may be why the

debate remains so interesting.

The question may not be whether AI is creative in

the same way humans are. It may be whether creativity

itself has more than one form.

3.4 Have We Misunderstood

Creativity All Along?

At this point, the question begins to turn around.

Instead of asking whether AI is truly creative, we might

ask something more surprising: What if creativity itself

has always involved recombination?

The more we look at human creativity, the more

this seems plausible.

A new recipe may come from combining familiar

ingredients in an unusual way. A scientiﬁc idea may

emerge by connecting concepts from two diﬀerent

ﬁelds. A novelist may take old themes—love, conﬂict,

mystery—and arrange them into a story no one has told

before.

In each case, something new appears, but it often

grows from what already existed.

Take a simple example. Imagine someone

combines classical Indian music with jazz improvisation

and creates a new musical style. The elements are not

entirely new, but the combination may be original and

creative.

This happens everywhere. Creativity often lies not

in inventing from nothing, but in seeing connections

others did not see. If that is true, then recombination

is not the opposite of creativity—it may be part of its

foundation. And this is where AI becomes interesting

again.

Generative models also combine patterns in

unexpected ways. They can link ideas from diﬀerent

domains, generate unusual associations, and produce

novel outputs. In that sense, they are doing something

that resembles at least one aspect of creativity.

Of course, human creativity may include things

machines lack—intention, emotion, lived experience,

purpose. Those still matter. But perhaps those are

layers built upon a more basic process: forming new

ideas through unexpected combinations. If so, the gap

between human creativity and machine generation may

not be as absolute as it ﬁrst appears. Maybe AI is

not replacing creativity. Maybe it is helping reveal

something about what creativity has always been. And

perhaps that is the most interesting possibility of all.

Because the question may not be: Can machines be

creative? It may be: Have we misunderstood creativity

all along?

3.5 Conclusion

The question of whether machines can be creative

may not have a simple yes-or-no answer. But perhaps

that is because the question itself is too narrow.

AI has pushed us to look more carefully at

something we often take for granted: what creativity

actually means. We tend to associate it with originality,

imagination, and uniquely human insight. Yet the

process of creation is often less mysterious and more

relational—it frequently grows through connections,

transformations, and experimentation.

Seen this way, generative AI is interesting not

because it settles the question of machine creativity,

but because it complicates it. Its ability to produce

novel ideas, unexpected combinations, and useful

inspiration challenges the assumption that creativity

belongs entirely outside computation.

At the same time, AI also reminds us that creativity

is more than novelty alone. Meaning, purpose, and

human experience still shape much of what we value in

creative work.

Perhaps, then, the real signiﬁcance of AI is not

that it has become creative in the human sense, but that

it has become a mirror—forcing us to examine what

originality, imagination, and invention have always

involved.

And maybe that is the deeper insight: The rise

of creative machines may be teaching us less about

machines— and more about creativity itself.

3.5 Conclusion

References

AI can make you more creative—but it has limits

The Role of AI in Art Creation

Yes — AI art is art

The Rise of AI Art: Creativity or Automation?

Mastering Creativity in Data Science & AI

About the Author

Jinsu Ann Mathew is a research scholar

in Natural Language Processing and Chemical

Informatics. Her interests include applying basic

scientiﬁc research on computational linguistics,

practical applications of human language technology,

and interdisciplinary work in computational physics.

Part III

Astronomy and Astrophysics

Plasma in the Interstellar Medium

by Abishek P S

airis4D, Vol.4, No.5, 2026

www.airis4d.com

1.1 Introduction

The interstellar medium (ISM) is far more complex

than just empty space sprinkled with gas and dust. It is a

dynamic environment made up of several distinct phases,

each with diﬀerent temperatures, densities, and states of

ionisation. For example, cold molecular clouds, dense

and rich in hydrogen molecules, are the birthplaces of

stars, while hot ionised regions, often created by massive

stars, glow brightly as nebulae. Between these extremes

lie warm neutral and ionised gases that ﬁll much of the

galactic disk. The ISM is constantly shaped by stellar

winds, radiation, and supernova explosions, which stir

and enrich it with heavier elements like carbon, oxygen,

and iron. This recycling process means that every new

generation of stars is formed from material that has

been processed by earlier stars, linking stellar evolution

directly to galactic evolution. In short, the ISM is not

just a passive backdrop; it is the active stage on which

the drama of star birth, life, and death unfolds, driving

the ongoing transformation of galaxies.

1.2

Plasma in the Interstellar Medium

Plasma in the interstellar medium is a fundamental

state of matter that shapes the physics of galaxies. It

arises primarily through ionisation, where ultraviolet

photons from hot stars, cosmic rays accelerated by

supernovae, or shock waves strip electrons from atoms.

This process produces a mixture of free electrons and

ions, creating a highly responsive medium that behaves

very diﬀerently from neutral gas. Ionisation is not

uniform across the ISM; dense molecular clouds remain

mostly neutral, while diﬀuse regions are highly ionised,

making the ISM a patchwork of environments where

ionisation and recombination compete dynamically.

Because plasma is electrically conductive, it

couples strongly with galactic magnetic ﬁelds.

This magnetisation means that plasma dynamics

are governed not by simple ﬂuid mechanics

but by magnetohydrodynamics (MHD), where

electromagnetic forces play a central role. Even though

galactic magnetic ﬁelds are relatively weak, they exert

signiﬁcant inﬂuence over the diﬀuse plasma, guiding

charged particles, shaping cosmic ray propagation,

and channelling ﬂows of ionised gas. Magnetised

plasma supports waves and turbulence, redistributing

energy and momentum across vast scales[1]. This

coupling explains ﬁlamentary structures in nebulae, the

conﬁnement of cosmic rays, and the regulation of star

formation.

Plasma is not a minor component of the cosmos

but the dominant one. Over 99% of visible matter in the

universe exists in plasma form, and the ISM exempliﬁes

this dominance. Hot ionised plasma ﬁlls galactic halos

and bubbles carved by supernovae, while even cooler

regions remain partially ionised. Plasma connects

local galactic environments to the larger cosmic web,

where intergalactic plasma forms ﬁlaments that structure

the universe itself. This dominance underscores why

plasma physics is central to astrophysics: understanding

plasma in the ISM is essential for modelling galaxy

evolution, star formation, and the transport of energy

and matter across cosmic scales.

1.4 Physical Processes in Interstellar Medium Plasma

1.3 Multi-phase Plasma Structure

The interstellar medium is a multi-phase plasma

structure, where diﬀerent temperature and density

regimes coexist and interact dynamically. This

framework is essential because it explains how energy

and matter circulate through galaxies, shaping their

evolution.

The Warm Ionised Medium (WIM) is one of the

most pervasive plasma phases. It consists of diﬀuse

ionised hydrogen at temperatures around 8,000 K, ﬁlling

much of the galactic disk. The WIM is maintained by

ultraviolet radiation from hot stars, which keeps the

gas ionised over large volumes[2]. The WIM is crucial

because it represents the baseline ionised environment

in which stars and cosmic rays interact, and it serves as

a reservoir of material that can cool and condense into

denser structures. The Hot Ionised Medium (HIM) is

created by supernova explosions, which inject enormous

amounts of energy into the ISM. With temperatures

reaching about

K, the HIM is extremely diﬀuse

but occupies large cavities known as “superbubbles.”

This plasma emits strong X-ray radiation, making it

observable by space telescopes. Researchers view the

HIM as a key feedback mechanism: it regulates star

formation by heating surrounding gas, drives turbulence,

and enriches the ISM with heavy elements produced in

stellar interiors. In contrast, the Cold Neutral Medium

(CNM) is not plasma but coexists with plasma phases. It

consists of cooler atomic hydrogen at temperatures near

100 K, often forming dense molecular clouds. These

clouds are the birthplaces of stars, and their interactions

with surrounding plasma phases are central to the star-

formation cycle. CNM cannot be studied in isolation,

because shocks, radiation, and turbulence from plasma

phases constantly reshape its boundaries and trigger

collapse into new stars.

Importantly, these phases are not isolated

compartments. They exchange energy and matter

through shocks, turbulence, and radiation. Supernovae

heat cold gas into plasma, while cooling processes

allow hot plasma to condense back into neutral clouds.

Turbulence mixes material across boundaries, and

radiation ionises neutral gas at the edges of molecular

clouds. This dynamic interplay is what makes the ISM

a “living system,” continuously recycling matter and

energy.

1.4 Physical Processes in Interstellar

Medium Plasma

The interstellar medium is governed by a set of key

physical processes that continually reshape its plasma

environment and regulate the life cycle of galaxies.

These processes, such as supernova feedback, cosmic

ray interactions, radiative cooling, and turbulence are

not isolated phenomena but interconnected mechanisms

that together determine the structure, dynamics, and

evolution of the ISM.

Supernova feedback is perhaps the most dramatic

driver of change in the ISM. When massive stars

explode, they inject vast amounts of energy into their

surroundings, producing shock waves that heat and

ionise gas. These shocks carve out superbubbles of

plasma, which can extend hundreds of parsecs across the

galactic disk. Supernova feedback is essential because it

prevents runaway star formation by dispersing cold gas,

while simultaneously enriching the ISM with heavy

elements synthesised in stellar interiors[3]. It is a

feedback loop: stars form from the ISM, and their

deaths reshape the very medium that gave them birth.

Cosmic ray interactions add another layer of

complexity. High-energy particles accelerated in

supernova shocks scatter oﬀ plasma, inﬂuencing the

pressure balance of the ISM. We note that cosmic rays

can travel long distances, coupling diﬀerent regions

of the galaxy and even escaping into intergalactic

space. Their interactions with plasma aﬀect transport

processes, magnetic ﬁeld dynamics, and the heating of

otherwise cold regions [4]. This makes cosmic rays not

just a byproduct of stellar explosions but an active agent

in galactic evolution.

Radiative cooling is the counterbalance to heating.

Plasma loses energy through line emission, such as

hydrogen recombination and transitions of metal ions,

such as oxygen and carbon. However, cooling in the

ISM is highly non-linear and often out of equilibrium.

1.6 Challenges

Studies highlight that cooling rates depend strongly

on density, metallicity, and ionisation state, meaning

that plasma can cool rapidly in some regions while

remaining hot in others[3]. This uneven cooling creates

the multi-phase structure of the ISM, where hot, warm,

and cold components coexist and interact.

Turbulence permeates the ISM, cascading energy

across scales from kiloparsec-sized superbubbles

down to parsec-sized clouds. Plasma turbulence is

magnetised, meaning that magnetic ﬁelds guide the ﬂow

of energy and create anisotropic structures. Turbulence

is a critical regulator of star formation: it can support

clouds against collapse, fragment them into smaller

structures, or trigger collapse in localised regions[1].

Turbulence also mixes chemical elements, ensuring that

enrichment from supernovae is distributed throughout

the galaxy.

1.5 Observational Evidences

The study of plasma in the interstellar medium

relies heavily on observational evidence across multiple

wavelengths, since plasma itself is invisible in ordinary

light. We have developed sophisticated techniques to

probe its properties, each revealing diﬀerent aspects of

the ISM’s complex structure and dynamics.

X-ray astronomy has been particularly

transformative in uncovering the presence of

hot plasma. Supernova remnants and galactic halos

emit strongly in X-rays, allowing astronomers to

measure temperatures of around a million degrees

Kelvin. These observations reveal the existence of

superbubbles carved by stellar explosions, as well

as diﬀuse hot gas ﬁlling galactic halos[5]. X-ray

data provide direct evidence of supernova feedback

and the large-scale circulation of energy in galaxies,

conﬁrming theoretical models of plasma heating and

enrichment. Radio astronomy oﬀers another window

into plasma physics, especially through the detection

of synchrotron radiation. This radiation is produced

when relativistic electrons spiral around magnetic

ﬁeld lines, highlighting the intimate connection

between plasma and galactic magnetism. Radio

observations map the structure of magnetic ﬁelds

across galaxies and trace cosmic ray interactions with

plasma. Researchers use these data to study turbulence,

magnetic conﬁnement, and energy transport, all of

which are critical for understanding how plasma shapes

galactic evolution. Optical and ultraviolet spectroscopy

provide complementary insights by identifying

ionisation states and plasma cloud densities. Emission

and absorption lines reveal the chemical composition,

ionisation balance, and physical conditions of the

ISM. For example, hydrogen recombination lines

indicate regions of active ionisation, while metal ion

transitions trace cooling processes. We rely on these

spectra to quantify plasma densities, measure ionisation

fractions, and test models of radiative cooling and

non-equilibrium processes.

Together, these multi-wavelength observations

form a coherent picture of the ISM as a dynamic plasma

environment. Each technique probes a diﬀerent regime,

X-rays capture the hottest plasma, radio waves trace

magnetic interactions, and optical/UV spectra reveal

ionisation and cooling. The synergy of these methods

is essential because plasma cannot be studied with

ordinary visible light alone. This multi-wavelength

approach is the cornerstone of modern astrophysics,

enabling us to piece together the life cycle of plasma in

galaxies and its role in shaping cosmic evolution[6].

1.6 Challenges

The study of plasma in the interstellar medium

presents several profound research challenges that

continue to push the boundaries of astrophysics. One of

the most signiﬁcant diﬃculties lies in non-equilibrium

ionisation. Unlike laboratory plasmas, which can

often be approximated as being in equilibrium, the

ISM is subject to constant disturbances from radiation,

shocks, and cosmic rays. This means that ionisation and

recombination processes do not balance neatly, and the

plasma often exists in transient states. This complicates

modelling eﬀorts, as standard equilibrium assumptions

fail to capture the ISM’s true behaviour. Instead,

complex time-dependent simulations are required to

track how ionisation evolves under varying conditions,

making the study of ISM plasma both computationally

1.7 Relevance

intensive and conceptually challenging.

Another major obstacle is the issue of coupled

physics. Plasma in the ISM cannot be understood within

a single discipline; it requires integrating atomic physics,

radiation transfer, hydrodynamics, and magnetic ﬁeld

dynamics. Each of these areas introduces its own

complexities. Atomic physics governs ionisation and

cooling processes, radiation transfer dictates how energy

moves through the medium, hydrodynamics describes

ﬂuid-like behaviour, and magnetic ﬁelds add anisotropy

and turbulence through magnetohydrodynamics (MHD).

Therefore, we build models that combine these diverse

physical processes into a uniﬁed framework. This

coupling is not only mathematically demanding but

also requires cross-disciplinary expertise, making ISM

plasma research a highly collaborative ﬁeld.

The scale problem further complicates matters.

Processes in the ISM range from microscopic

electron collisions to galactic-scale phenomena such

as superbubbles created by supernovae. Capturing

this enormous range of scales in a single model

is nearly impossible with current computational

resources. Therefore we must rely on multi-scale

simulations, where small-scale physics is parameterised

and embedded within larger-scale models. Even then,

uncertainties remain, as small-scale processes such as

turbulence and cosmic-ray scattering can have outsized

eﬀects on galactic evolution. The challenge lies in

bridging these scales without losing essential physical

detail, a task that continues to drive innovation in

computational astrophysics.

1.7 Relevance

The importance of plasma in the interstellar

medium lies in its role in regulating the fundamental

processes that shape galaxies. One of the most critical

roles plasma plays is in regulating star formation.

Molecular clouds, the birthplaces of stars, are subject to

the pressures and turbulence generated by surrounding

plasma. If plasma pressure is too high, it can

prevent these clouds from collapsing under gravity,

delaying or suppressing star formation[5]. Conversely,

turbulence within plasma can fragment clouds, creating

localised regions where collapse becomes possible. We

emphasise that this delicate balance between plasma

support and gravitational collapse determines the rate

of star formation, making plasma a key regulator of

galactic growth.

Plasma also functions as the backbone of the

galactic ecosystem, transporting energy and metals

across vast distances. When stars die in supernova

explosions, they release heavy elements such as carbon,

oxygen, and iron into the ISM. Plasma carries these

enriched materials throughout the galaxy, ensuring

that subsequent generations of stars and planets are

chemically diverse. In addition, plasma distributes

energy from stellar winds and supernovae, maintaining

the multi-phase structure of the ISM. Studies view this

transport as a vital feedback mechanism: it links the

life cycles of individual stars to the broader dynamics

of the galaxy, creating a self-sustaining system in which

matter and energy are constantly recycled.

On an even larger scale, plasma in the ISM is

central to cosmic evolution. Galaxies are not static

structures; they evolve over billions of years through

cycles of star birth, death, and renewal. Plasma is the

medium through which these cycles occur, connecting

small-scale processes like ionisation and turbulence

to large-scale phenomena such as galactic winds and

halo formation. By studying ISM plasma, researchers

gain insights into how galaxies recycle matter, regulate

star formation, and interact with the intergalactic

medium. This knowledge is crucial for understanding

the evolution of the universe itself, since plasma is the

dominant state of visible matter across cosmic scales.

References

[1] Ferri

ere, K. (2020). “Plasma turbulence in the

interstellar medium”. Plasma Physics and Controlled

Fusion, 62(1), 014014.

[2] Reynolds, R. J. (1992). “The warm ionized

medium”. AIP Conference Proceedings (Vol. 278, No.

1, pp. 156-165). American Institute of Physics.

[3] Klessen, R. S., & Glover, S. C. (2015).

“Physical processes in the interstellar medium”. IStar

Formation in Galaxy Evolution: Connecting Numerical

1.7 Relevance

Models to Reality: Saas-Fee Advanced Course 43.

Swiss Society for Astrophysics and Astronomy (pp. 85-

249). Berlin, Heidelberg: Springer Berlin Heidelberg.

[4] Spitzer Jr, L. (2008). “Physical processes in

the interstellar medium”. John Wiley & Sons.

[5] Maciel, W. J. (2013). “Astrophysics of the

interstellar medium (Vol. 1)”. New York: Springer.

[6] Lequeux, J. (2005). “The interstellar medium”.

Berlin, Heidelberg: Springer Berlin Heidelberg.

About the Author

Abishek P S is a Research Scholar in

the Department of Physics, Bharata Mata College

(Autonomous) Thrikkakara, kochi. He pursues

research in the ﬁeld of Theoretical Plasma physics.

His works mainly focus on the Nonlinear Wave

Phenomenons in Space and Astrophysical Plasmas.

Astronomy at Scale

by Ajay Vibhute

airis4D, Vol.4, No.5, 2026

www.airis4d.com

2.1 Growth of Astronomical Data

Volumes

Over the past few decades, astronomy has

undergone a dramatic transformation driven by the

exponential growth of data volumes. Early digital

surveys produced datasets measured in megabytes

or gigabytes, but modern observatories routinely

generate terabytes of data per night, with total archives

reaching petabyte scales. This growth is fueled

by advances in detector technology, wider ﬁelds of

view, higher temporal resolution, and multi-wavelength

observational capabilities.

Large-scale projects such as wide-ﬁeld sky surveys

and radio interferometers continuously scan the sky,

producing repeated measurements of billions of

sources. Time-domain astronomy further ampliﬁes

data volume by capturing transient and variable

phenomena, requiring frequent observations and rapid

data processing. As a result, astronomy has become

a data-intensive science, where the scale of data is

comparable to that encountered in ﬁelds such as particle

physics or climate science.

This increase in volume is not merely quantitative

but also qualitative. Modern datasets are richer,

combining spatial, spectral, and temporal information,

often with complex metadata describing observational

conditions and calibration parameters. Managing

and extracting knowledge from such datasets requires

fundamentally new computational approaches.

2.2 Why Manual Analysis Failed

The traditional model of manual data

analysis—where astronomers visually inspected images

or individually processed small datasets—became

untenable as data volumes increased. Even a single

modern survey can contain more images than a

human could examine in a lifetime, rendering manual

inspection impractical for all but a tiny subset of data.

Human-driven analysis is also limited by

subjectivity and inconsistency. Diﬀerent researchers

may interpret the same data diﬀerently, leading

to potential biases and reduced reproducibility.

Furthermore, manual methods are inherently slow and

cannot keep pace with the continuous data streams

produced by modern instruments, particularly in time-

sensitive applications such as transient detection.

As datasets grew, it became clear that automation

was not simply a convenience but a necessity.

Algorithms replaced human inspection for tasks

such as source detection, classiﬁcation, and anomaly

identiﬁcation. Machine learning techniques, in

particular, have enabled the rapid analysis of large

datasets, identifying patterns and rare events that would

be diﬃcult or impossible for humans to detect unaided.

2.3 Data-Intensive Astronomy

Data-intensive astronomy represents a shift in

how scientiﬁc discovery is conducted. Instead of

focusing on individual objects, researchers increasingly

analyze large populations, searching for statistical

trends, correlations, and rare outliers across vast

2.5 Scientiﬁc Implications of Scale

datasets. This approach enables new types of science,

such as mapping the large-scale structure of the universe,

studying the evolution of galaxies over cosmic time,

and identifying transient phenomena like supernovae

or gravitational wave counterparts.

The scale and complexity of these datasets

require advanced computational techniques, including

distributed computing, parallel processing, and machine

learning. Algorithms must be designed not only for

accuracy but also for scalability, ensuring that analyses

remain feasible as data volumes continue to grow.

In this paradigm, data are often processed

in automated pipelines that perform tasks such as

calibration, source extraction, feature measurement,

and classiﬁcation. These pipelines must be robust,

reproducible, and capable of handling heterogeneous

data from multiple instruments and observing modes.

Data-intensive astronomy thus blurs the line between

astronomy and data science, integrating methods from

statistics, computer science, and applied mathematics

into the core of astronomical research.

2.4 Storage, Access, and Data

Movement

Handling astronomical data at scale presents

signiﬁcant challenges in storage, access, and data

movement. Petabyte-scale archives require distributed

storage systems that ensure reliability, redundancy, and

long-term preservation. Eﬃcient indexing and query

systems are essential for enabling researchers to locate

and retrieve relevant subsets of data without scanning

entire datasets.

Data access models have evolved to address these

challenges. Instead of downloading large datasets

locally, researchers increasingly analyze data in situ,

using remote computing resources located near the

data archives. Cloud computing and high-performance

computing (HPC) facilities provide the infrastructure

needed to process large datasets eﬃciently.

Data movement is another critical consideration.

Transferring large volumes of data across networks can

be time-consuming and costly, necessitating careful

optimization and, in some cases, the development

of specialized data-transfer protocols. Minimizing

unnecessary data movement by bringing computation

to the data has become a key design principle in modern

astronomical computing.

2.5 Scientiﬁc Implications of Scale

The shift to large-scale data has profound

implications for scientiﬁc discovery. On one hand,

it enables unprecedented statistical power, allowing

researchers to detect subtle eﬀects and rare phenomena

with high conﬁdence. Large datasets also support cross-

disciplinary studies, combining observations across

wavelengths and time to build a more comprehensive

picture of the universe.

On the other hand, the scale of data introduces

new challenges. Systematic errors can propagate across

entire datasets, potentially biasing results if not properly

controlled. The complexity of analysis pipelines makes

it more diﬃcult to fully understand how results are

derived, increasing the importance of transparency,

validation, and reproducibility.

Moreover, the reliance on automated methods

raises important questions about interpretability and

trust. Machine learning models, while powerful, can act

as ”black boxes,” making it diﬃcult to understand the

reasoning behind their outputs. Ensuring that results are

physically meaningful and not artifacts of the analysis

process remains a central concern.

Ultimately, astronomy at scale requires a careful

balance between leveraging computational power and

maintaining rigorous scientiﬁc standards.

2.6 Summary

Astronomy has entered an era deﬁned by data scale.

The rapid growth of observational data has transformed

the ﬁeld from one centered on individual measurements

to one driven by large datasets and statistical analysis.

Manual methods have given way to automated pipelines

and machine learning, enabling the eﬃcient extraction

of knowledge from vast and complex data.

2.6 Summary

At the same time, the challenges of storage,

access, and systematic error highlight the need

for robust computational infrastructure and careful

methodological design. As data volumes continue

to grow, the integration of computation into every

aspect of astronomy will deepen, reinforcing its role

as a foundational component of modern scientiﬁc

discovery.

About the Author

Dr. Ajay Vibhute is currently working

at the National Radio Astronomy Observatory in

the USA. His research interests mainly involve

astronomical imaging techniques, transient detection,

machine learning, and computing using heterogeneous,

accelerated computer architectures.

Black Hole Stories-26

Some Black Hole Mergers From

LIGO-Virgo-KAGRA Observing Run O4

by Ajit Kembhavi

airis4D, Vol.4, No.5, 2026

www.airis4d.com

In this story we will consider some interesting

binary mergers detected during the observing runs

O4 of the LIGO-Virgo-KAGRA detectors. Our

description here will depend on the concepts developed

in describing O3 sources.

3.1 The Fourth Observing Run O4

The observing run O4 began on May 24, 2023 and

ended on November 18, 2025. O4 was divided into

three parts, with O4a from 24 May 2023 to 16 January

2024, O4b from 10 April 2024 to 28 January 2025, and

O4c starting directly after O4b on 28 January 2025, and

ending on 18 November 2025, with a commissioning

break between 01 April 2025 to 11 June 2025. There

have been 218 conﬁdent detections of merging binary

systems in the run O4a, with another 173 candidate

events during O4b and O4c, which remain to be

conﬁrmed through detailed data analysis (see BHS-

22 for a more detailed discussion). This story will be

about a few interesting binary mergers detected during

O4.

This source was detected during O4a by the two

aLIGO detectors on November 23, 2023. The Virgo and

KAGRA detectors were not observing at that time. The

event was observed over about ﬁve cycles, over a short

duration of about 0.1 s, but data from the two detectors

enabled the detection to be made with high conﬁdence.

However, there was relatively large uncertainty in the

determination of the source parameters, with diﬀerent

methods of analysis providing a range of values.

From the analysis, the primary is of 137 (119,

160) Solar masses, where 137 is the median value of

the range of the primary mass obtained in the analysis,

and the numbers in parenthesis show the range of in

which the primary mass is located with 90 percent

probability. We have indicated the ranges to show the

large uncertainty in the parameter values for this source.

With the same notation, the secondary has 101 (51,

123) Solar masses, with the total mass being 236 (189,

266) Solar masses. The ﬁnal mass after the merger is

222 (180, 250) Solar masses. It was found the primary

had dimensionless spin parameter 0.9 (0.71, 1.0) and

the secondary 0.80 (0.28, 1.0). The spin parameter of

the remnant is 0.84 (0.68, 0.90). The spin parameter is

deﬁned in the description of the GW190412 in BHS-25.

The distance to the source is 2.2 (0.7, 4.1) Gigaparsec.

It follows from the number above that the

GW231123 was a merger of two high mass black

holes with high spin. The total mass of the binary

is greater than that of the massive binary GW190521

described in BHS-25, making GW231123 the most

massive binary merger detected as of the end of 2025.

The high total mass, together with the high spin of the

two components, make analysis diﬃcult because the

theoretical models which are used in the analysis are

not completely adequate. That leads to diﬀering values

of parameters predicted by diﬀerent models and with

3.1 The Fourth Observing Run O4

resultant large uncertainty.

We have considered in BHS-25 a gap in the mass

of black holes in the region of 60 – 130 Solar masses.

Black holes produced through the evolution of massive

stars are not expected to have mass in this region. Given

the high mass of the primary and secondary components

of the binary which merged to form GW231123, it can

be argued that there is a signiﬁcant probability that the

mass of the primary, or the secondary, or both are in the

mass gap region, so we need to understand how such

black holes can be produced. A few possibilities are:

(1) There can be change in the details of the evolution

of progenitor massive star, which avoids the formation

of a pair instability supernova (PISN). That leads to the

collapse of the core of a massive star to a black hole

with mass in the gap; (2) the massive black hole binary

components could have formed through earlier BH-BH

mergers which would produce high mass black holes

with high spin; (3) The black hole mass could have

increased signiﬁcantly by accretion of gas in a suitable

environment or in collisions with other stars. There

are other possible processes too for producing black

holes in the mass gap, but all the process need detailed

study before their suitability can be established. The

discovery of other massive binaries in the existing data

or through future observations will help in establishing

viable processes.

All the gravitational wave detections made so far

have been analysed under the assumption that they

are mergers of compact binaries, with the components

being black holes or neutron stars, and the orbit having

low eccentricity, i.e., it is nearly circular. The same

assumption has been made in the case of GW231123

and it provides a good ﬁt to the observed signal. But

given the large uncertainty in the parameter values, and

the small number of cycles on which the ﬁt is based, it

is necessary to consider other possible ways to produce

the observed gravitational wave signal. Models with

high eccentricity may have to be considered, but it is

astrophysically unlikely that compact binaries with high

eccentricity can be produced through earlier mergers.

Some possible sources which could produce a burst of

gravitational waves of the duration observed are core-

collapse supernovae, cosmic strings and collisions of

compact objects made up of exotic particles like axions.

Some of these possibilities are wholly theoretical at the

present time, and the merger of massive black holes

in a binary system remains the most likely explanation

of the event. The alternative interpretations of the

gravitational wave data mentioned here also apply to

the massive binary GW190521detected in O3.

GW250104: Detected on January 4, 2025, this

merging binary has source parameters similar to those

of the ﬁrst ever gravitational wave detection GW150914.

But the sensitivity of the LIGO detectors during O4 was

much greater than during the ﬁrst observing run O1,

which happened ten years before O1. So, the observed

signal for GW250104 is much larger than the noise in

the detector, which is expressed as a high signal-to-noise

ratio. This leads to a very clean signal from which ﬁrm

conclusions can be drawn about the theory of gravity

and black holes.

GW250114 was observed by both LIGO detectors,

while the Virgo and KAGRA were not operational.

The primary, secondary and total mass determined

for the merging binary are

= 33.6, m

= 32.2

and

= 65.8

Solar masses, respectively. The ratio of the

secondary mass to the primary mass is 0.91. The two

black holes before the merger have low spin. The mass

of the remnant black hole after the merger is

= 62.7

Solar masses and it has a spin parameter

= 0.68

The mass lost in the merger to gravitational waves is

3.1 Solar masses. These parameters are similar to

those of many black hole binaries detected earlier. The

novel element in this case is the high signal-to-noise

ratio, which makes possible increased accuracy in the

determination of the parameters.

The gravitational wave signal observed by the

aLIGO detector at Hanford from the ﬁrst merging

binary GW150815 in 2015 (upper panel), and the signal

observed by the upgraded more sensitive version of the

detector from GW250104 (lower panel) are shown in

Figure 1. Both binaries were located about 400 Mpc

away, and the black holes in the two binaries were of 30

to 40 Solar masses. The energy emitted in each merger

therefore was about the same. The vertical purple bars

indicate the data point for a given time as the event

progresses, with the height of the bar indicating the

3.1 The Fourth Observing Run O4

Figure 1: The signal observed by the aLIGO detector

at Hanford from the merging binary black holes

GW150915 in 2015 (upper panel) and GW250141

(lower panel) in 2025. See the text for a detailed

description.

detected noise at that time. The green lines in each

panel is the best ﬁt obtained for the data using a model

based on the general theory of relativity. The reduced

noise level and the smoother ﬁt obtained in 2025 are

evident.

Soon after the merger the resultant black hole is

a distorted object, which rings down to its ﬁnal state

of a Kerr black hole. The ringdown signal consists

of quasi-normal modes, which are oscillations which

damp down as the blackhole settles to its ﬁnal state.

The oscillation frequencies and the time over which the

damping occurs are determined by the mass and spin of

the ﬁnal black hole. The dominant mode is quadrupolar

in nature, which is expected given that the two black

hole masses are nearly equal (see the discussion about

multipole moments for the O3 detection GW190412).

At late times, the ringdown can be represented by two

modes, an oscillation with a single frequency and its

ﬁrst overtone, both of which are damped. The nature

of the oscillations including the existence of the two

modes closely resembles the expectation for a Kerr

black hole with the mass

and spin parameter

mentioned above.

The high quality data available for the GW250104

merger can be used to test the second law of black hole

mechanics, which was given by Stephen Hawking in

1971, and was also discussed earlier by others. This law

states that the area of the event horizon of a black hole

cannot decrease in time through any processes, it must

remain constant or increase during any interactions that

the black hole undergoes. The law can only be violated

if certain general conditions are not met, and in some

theories of gravity other than Einstein’s general theory

of relativity. For black hole binaries we have been

considering, it follows from Hawking’s law that the

area of the remnant black hole must be greater than the

sum of the areas of the two black holes of the binary

before the merger. The total area before the merger can

be measured from the detected signal before the merger,

which is the part of the signal when the two black

holes are spiralling towards each other. The area of the

remnant can be measured from the ringdown part of the

signal after the merger is complete. Such measurements

exclude the signal from the most violent parts of the

merger, The analysis is based on the assumption that

the orbit of the black holes during the merger was

almost circular in shape, that general theory of relativity

applies in the region where the measurements are made,

and that the Kerr solution provides a good description

of black holes. It is found that the results make it

highly probable that the area of the remnant was indeed

greater than the sum of the areas of the two black holes

before the merger. As of early 2026, the signal from

GW250114 is being analysed for further precise tests of

the general theory of relativity and the Kerr space-time.

GW241011 & GW241110: These two mergers

are similar in properties and possible origin so we treat

them together, as was done in the discovery research

publication. GW241011 was detected on October 11,

2024 by LIGO, Hanford and by Virgo, while GW241110

was detected by the LIGO detectors at Hanford and

Livingston, and Virgo. For both sources the probability

for the detection to be a false alarm is very low, and the

signal is strong so that accurate parameter determination

is possible.

For GW241011, in units of Solar masses, the

primary black hole mass is 19.6, the secondary black

hole mass 5.9 and the ratio of the secondary mass to

primary mass is 0.3. The novel element for this source

is the spin of the primary which has been precisely

determined to have the large value 0.78 in dimensionless

units (see the discussion for GW190412 for a deﬁnition

of the dimensionless spin). This value makes the

primary the fastest rotating black hole detected by early

2026. There is an angle of about 31 degrees between

3.1 The Fourth Observing Run O4

the direction of the spin vector of the primary and the

direction of the angular momentum of the binary system.

It is not possible to determine the spin of the secondary

from the data.

In the case of GW241110, in Solar units, the

primary mass is 17.2, the secondary mass 7.7, the mass

ratio is 0.45 and the dimensionless spin of the primary is

0.61. The angle between the spin vector of the primary

and the orbital angular momentum is 133 degrees, so

that in this case there is a large misalignment between

the two directions. This is the ﬁrst black hole binary

known in which the spin is so misaligned with the

angular momentum. The mass and spin values are

rather similar for the two mergers we are considering

here.

The favoured explanation for the high spin is that

the two black hole binaries are formed from black holes

which have themselves formed out of earlier mergers.

The scenario here is that massive black holes are formed

through mergers of black hole binaries. Such remnant

black holes have high mass and also high spin, in the

dense environment of the central region of a star cluster.

Such remnant black holes have high spin which arises

from the angular momentum of the binary which merged.

In fact, the spin parameter of the remnants of binary

mergers which have been observed so far is clustered

around the value of about 0.7. The binaries which are

later formed from such black holes would have spin

and unequal mass components, as has been observed in

the case of the two binary mergers we are considering

here. Hierarchical merging therefore provides a natural

explanation for the formation of such binaries. The

hierarchical formation mechanism is shown in Figure 2.

As explained in the case of the observing run O3

merger GW190412, the most dominant component of

gravitational wave radiation is quadrupole in nature.

As the secondary to primary mass ratio becomes

signiﬁcantly less than 1, higher multipoles contribute

to the radiation, and can be detected if the signal is

strong enough. In the case of the two binary mergers

GW241011 and GW241110, the mass ratios are 0.3

and 0.45 respectively and the signal is loud, so higher

moments can be detected. In addition, the high spin

of the primary component of the two sources allows

Figure 2: The hierarchical merger route for the

formation of black hole binaries. The formation takes

place in the dense central environment of a large star

cluster, where there are frequent encounters between

black holes and other constituents of the cluster. On

the left is shown how GW241011 could have formed.

In the ﬁrst generation at the bottom, a 13.3 Solar mass

black hole merges with a 7.5 Solar mass black hole. The

eﬀective spin of the system is 0.23. These parameters

are possible values derived from detailed modelling and

can change with the model. The merger produces a black

hole remnant which has the mass and spin observed for

the primary component of the second generation binary.

The remnant also has a velocity which is estimated to be

750 km/s, but with large uncertainty. The new remnant

captures another black hole with mass 5.9 Solar masses,

to produce the next generation binary, whose merger

we observe as GW241011. On the right of the ﬁgure

a similar process for the formation of GW241110 is

shown.

other tests to be performed. The spin of the black hole

contributes to the quadrupole moment, and there is a

speciﬁc relation between the two for a Kerr black hole.

From the gravitational wave data, it is veriﬁed that this

relation holds accurately, establishing the validity of

the Kerr solution and the general theory of relativity in

this case. The measurements are good enough to rule

out the possibility that the spinning compact object is

not a Kerr black hole and is instead an exotic object like

a boson star.

GW230529: This source was observed on 29

May 2023 by the aLIGO detector at Livingston alone.

The aLIGO detector at Hanford and Virgo were oﬄine,

and the sensitivity of the KAGRA detector was not

enough to contribute to the observation.

This is a low mass binary merger, with the primary,

secondary and total mass being 3.6, 1.4 and 5.1 Solar

masses, and a secondary to primary mass ratio of 0.39.

The spin parameter of the primary is 0.44 and the source

distance is 201 Megaparsec. The mass of the primary

3.2 Compact Object Masses Found in the Four Observing Runs:

means that it could be a black hole, or an unusually

massive neutron star. The mass of the primary puts

it in the region of (3 – 5) Solar masses, which is a

known low mass gap in which neither neutron stars, nor

black holes are found. The primary could be a black

hole in the gap, or an unusually massive neutron star,

again in the gap. The mass of the secondary, including

uncertainties in the mass measurements, puts it in the

known range of neutron star masses. If either or both

components are neutron stars, then it is possible that

the tidal distortions produced in them make could be

detected in the gravitational wave signal. But no sign of

such distortion has been found in the primary, and the

data is not suﬃcient to constrain any deformations in the

secondary. The overall evidence points to the merger

being that of a black hole and a neutron star. With

one component being a neutron star, it is possible for

electromagnetic signals to be emitted by the disruption

of the neutron star during the merger. However, no

such electromagnetic counterpart has been found. The

gravitational wave signal was detected by only one

detector, so the position of the source in the sky was

very poorly determined. In such a case it is very

diﬃcult to associate an electromagnetic counterpart

like a Gamma-ray burst with the gravitational wave

detection.

GW230529 is the ﬁrst merging binary detected in

which the primary candidate is very likely a black hole

with mass in lower mass gap region (we have discussed

in BHS-25 gap at larger masses, in the 60 – 130 Solar

mass range). This provides clues to the origin of BHNS

binary, but the possibilities are still open as of early

2026.

3.2 Compact Object Masses Found in

the Four Observing Runs:

Here we repeat a diagram and comments from

BHS-22, in the context of the present story and BHS-

25.

In the observing runs O1, O2, O3, which included

a total observing period of 23 month, there were 90

gravitational wave source detections. In O4, more than

Figure 3: In this plot the numbers and masses

of the black holes and neutron stars discovered as

components and products of merging binaries by the

LVK collaboration. Also shown are black holes and

neutron stars discovered through electromagnetic means.

See text for details. Image Credit: LIGO-Virgo-

KAGRA | Aaron Geller | North Western University.

200 candidate sources have been discovered by the end

of the run in November 2025, over an observing period

again of 23 months. Some of the candidate sources

have been conﬁrmed to be merging black hole binaries,

while the rest are being steadied in detail to conﬁrm

them as valid sources or to reject them.

In Figure 3 are shown the numbers and masses

of black holes and neutron stars discovered through

gravitational wave detections and electromagnetic

means. Black holes are shown mainly as blue pairs,

connected by a blue line. These are components of

binary black hole systems which are merging and are

observed as gravitational wave source (GW). The blue

line extends to the black hole which is the product

of the merger of the two components. The mass of

each black hole is indicated on the vertical axis. The

orange dots are neutron stars which are components of

a binary neutron star system, or of a black hole–neutron

star binary, which have been detected by LIGO. The

yellow dots represent neutron stars discovered through

electromagnetic means (EM). Such EM detection is

possible (1) from the X-ray emission by a binary system

of which a neutron star is a component, the other

component being a star from which the neutron star

is accreting matter or (2) from a binary neutron star

when one (or both) component is a radio pulsar. The

pink dots are black holes discovered through their being

the compact component of an X-ray binary system, the

other component again being a star from which the

3.2 Compact Object Masses Found in the Four Observing Runs:

black hole accretes mater (see BHS-1 for some details).

It is seen from Figure 3 that the mass range of the

GW and EM neutron stars are the same, extending over

1-2 Solar masses. While there is an overlap between

GW and EM black hole mass ranges, the GW black

hole masses extend to signiﬁcantly higher values. The

EM black holes are the end products of the evolution

of stars with mass greater than about 25 Solar masses.

They are the compact objects left after the supernova

explosion which occurs at the end of the evolution of

the massive star. This explanation can apply to the

formation of the less massive GW black holes, with the

added complication that binary black hole formation

has to take place without disrupting the binary of which

they are the components. As for the more massive

GW black holes, there are diﬃculties in explaining

their origin as end products of stellar evolution, and

other mechanisms like hierarchical mergers have to be

considered, as described above.

The black hole binary mergers discovered in the

O1 and O2 had similar parameters. There was one

gravitational wave detection of a spectacular binary

neutron star merger observed in O2, with accompanying

electromagnetic radiation. In later runs here have been

a few other mergers identiﬁed as NS-NS and BH-NS,

but no electromagnetic counterpart has been identiﬁed

in these cases. That has primarily been because of the

diﬃculty in localising the position of the merger to a

small enough area of the sky which could be searched

for the counterpart. This situation will change when

LIGO-India becomes operational around 2030. With

the additional detector with high sensitivity operating at

a distance from the three detectors presently in operation,

localisation will improve signiﬁcantly, leading to further

identiﬁcations.

As the sensitivity of the gravitational wave

detectors has improved over the four runs, a variety

of mergers have been detected, some which we have

captured in the descriptions of individual sources from

the later runs. It is clear that as the sensitivity improves

further, and new generations of detectors become

available on the ground and in space, we will make

rich discoveries which will provide us insights into the

formation, evolution and ﬁnal moments of these sources.

It is startling to imagine that so many of these very

massive binaries are present in the Universe, which we

will ever detect only through their gravitational radiation,

mostly in the last few moments of their evolution.

About the Author

Professor Ajit Kembhavi is an emeritus

Professor at Inter University Centre for Astronomy

and Astrophysics and is also the Principal Investigator

of the Pune Knowledge Cluster. He was the former

director of Inter University Centre for Astronomy and

Astrophysics (IUCAA), Pune, and the International

Astronomical Union vice president. In collaboration

with IUCAA, he pioneered astronomy outreach

activities from the late 80s to promote astronomy

research in Indian universities.

X-ray Astronomy: Theory

by Aromal P

airis4D, Vol.4, No.5, 2026

www.airis4d.com

4.1 Introduction

In the previous article, we discussed X-ray

emission from accretion disks in X-ray binaries and

compared its eﬃciency to that of nuclear fusion. We

concluded by asking whether the accretion disk is the

only source of X-ray emission in X-ray binaries. In

today’s article, we will focus on a speciﬁc type of X-

ray emission found in Neutron Star Low-Mass X-ray

Binaries: the thermonuclear X-ray burst. As a personal

note, my research centers on the observational studies

related to this phenomenon.

4.2 Thermonuclear X-ray burst

We have already discussed how accretion disks

form in X-ray binaries. In neutron star X-ray binaries,

if the neutron star is relatively old and has a weaker

magnetic ﬁeld, the accreted material will eventually be

deposited onto its surface. If this accumulated matter

undergoes an unstable nuclear reaction, it can burn

instantaneously due to nuclear processes, leading to a

thermal runaway.

This thermal runaway happens because the

accreted hydrogen and helium fuel experiences intense

hydrostatic compression as it continues to pile onto

the surface of the neutron star. Within hours or

days, the accumulating material reaches extreme

ignition temperatures and densities. The core physical

mechanism driving this violent eruption is known as

the thin-shell instability.

Since the burning layer is exceptionally thin, only

a few meters deep compared to the neutron star’s typical

radius of around 10 kilometers, the initial nuclear

heating causes the shell to expand, but this is insuﬃcient

to reduce the local pressure and cool the region.

Additionally, in this dense environment, the electrons

are mildly degenerate, meaning their pressure does

not signiﬁcantly depend on temperature. This further

prevents the expanding gas from eﬀectively dissipating

the heat generated. As a result, the temperature spikes

dramatically, accelerating nuclear reaction rates and

triggering a localized thermonuclear runaway.

The speciﬁc dynamics of this thermonuclear

runaway depend heavily on the mass accretion rate

and the chemical composition of the infalling material.

At relatively low accretion rates, hydrogen burns

unstably via the Carbon-Nitrogen-Oxygen (CNO) cycle,

which can subsequently trigger helium ignition. At

intermediate mass accretion rates, hydrogen burns

continuously and stably via the hot CNO cycle,

completely exhausting itself and gradually building

up a dense, pure helium layer beneath it. Once critical

conditions are met, this pure helium layer violently

detonates via the triple-alpha process, producing a

short, intense X-ray burst that typically lasts around

ten seconds. At even higher accretion rates, the

conditions for helium ignition are met much faster,

before the hydrogen has fully burned, resulting in a

mixed hydrogen and helium ﬂash. The initial helium

ignition generates extreme temperatures that trigger

breakout reactions, bypassing the standard CNO cycle

and initiating the rapid-proton (rp) process. During

the rp-process, a rapid succession of proton captures

and slower beta decays synthesizes heavier elements,

signiﬁcantly extending the energy release and creating

4.2 Thermonuclear X-ray burst

Figure 1: Graphical representation of occurrence of

thermonuclear X-ray Burst

a burst tail that lasts for tens to hundreds of seconds.

The ignition itself is highly complex; rather than

erupting uniformly across the entire sphere, the runaway

typically sparks at a localized point, often near the

equator, where the eﬀective surface gravity is slightly

reduced by the star’s rapid rotation. From this ignition

point, a thermonuclear ﬂame front propagates laterally

across the neutron star, engulﬁng the entire surface in

approximately one second. In the most powerful of these

events, the extreme local luminosity can temporarily

exceed the Eddington limit, causing the outward

radiation pressure to overcome the immense inward

gravitational pull. When this threshold is breached,

the outermost layers of the neutron star’s photosphere

are physically lifted oﬀ the surface and driven outward,

creating a Photospheric Radius Expansion (PRE) burst.

As the thermonuclear fuel is exhausted, the lifted

photosphere eventually contracts and settles back onto

the stellar surface before an extended cooling phase

takes over. Ultimately, while continuous accretion

generates a steady baseline X-ray luminosity, the sudden,

localized burning of stored fuel temporarily outshines

this persistent emission by a factor of ten or more,

producing thermonuclear X-ray bursts

Thermonuclear X-ray emission is predominantly

thermal and is well-described by a blackbody spectrum

with peak temperatures reaching 2–3 keV. Consequently,

bursts are most prominent in the soft X-ray energy

band (typically below 10 keV), where they temporarily

outshine the persistent X-ray emission from accretion

by an order of magnitude or more

The intense photons from a thermonuclear X-

ray burst drastically alter the surrounding accretion

environment, primarily aﬀecting the corona, the

accretion disk, and the companion star. When the

burst injects a massive inﬂux of soft X-ray photons into

the hot electron corona, it triggers inverse Compton

scattering that rapidly cools the coronal plasma. This

cooling manifests observationally as a sharp deﬁcit

in hard X-ray emission during the burst peak. In

extreme cases, the burst’s immense radiation pressure

may completely blow the corona away, which could

temporarily shut oﬀ the collimated radio jets that are

linked to coronal magnetic ﬁelds. The burst also

strongly impacts the physical structure of the accretion

disk. Irradiation can heat the disk, causing it to puﬀ

up and increase its scale height, while the intense

photon ﬂux can induce Poynting-Robertson radiation

drag, forcing the inner disk material to drain onto the

neutron star. Furthermore, burst photons scattering oﬀ

the inner accretion disk generate observable reﬂection

features, such as ﬂuorescent iron emission lines and

absorption edges. Finally, photons that reach the cooler

outer accretion disk and the donor star are reprocessed,

producing transient optical and ultraviolet ﬂashes.

Studying thermonuclear X-ray bursts provides a

unique astrophysical laboratory to probe matter and

physical processes under extreme conditions that cannot

be replicated on Earth. One of the primary motivations

for studying these powerful explosions is to constrain

the Equation of State (EoS) of the supranuclear dense

matter inside neutron star cores. By using methods

like Photospheric Radius Expansion (PRE) bursts

and continuum spectrum modeling, researchers can

accurately measure a neutron star’s mass and radius,

thereby ruling out speciﬁc theoretical models of exotic

matter. Thermonuclear Burst allows astronomers to

dynamically probe the accretion process by observing

how burst photons interact with the accretion disk

and the hot electron corona. Finally, they provide a

testing ground for complex nuclear physics, such as

the rapid-proton (rp) process, and multidimensional

hydrodynamics like ﬂame spreading.

We will discuss more methods of producing X-rays

in the upcoming articles.

Reference:

Sudip Bhattacharyya Measurement of neutron

star parameters: A review of methods for low-

mass X-ray binaries

Anna L. Watts Thermonuclear burst oscillations

Nathalie Degenaar, David R. Ballantyne, Tomaso

4.2 Thermonuclear X-ray burst

Belloni, Manoneeta Chakraborty, Yu-Peng Chen,

Long Ji, Peter Kretschmar, Erik Kuulkers, Jian

Li, Thomas J. Maccarone, Julien Malzac, Shu

Zhang, and Shuang-Nan Zhang Accretion disks

and coronae in the X-ray ﬂashlight

Duncan K. Galloway and Laurens Keek

Thermonuclear X-ray bursts

Duncan K. Galloway, Zac Johnston, Adelle

Goodwin, and Alexander Heger High-energy

transients: thermonuclear (type-I) X-ray bursts

L. Colleyn, Z. Medin, and A. C. Calder Modeling

X-ray Bursting Neutron Star Atmospheres

W. H. G. Lewin, J. van Paradijs, and R. E. Taam

X-ray bursts

About the Author

Aromal P is a research scholar in

Department of Astronomy, Astrophysics and Space

Engineering (DAASE) in Indian Institute of

Technology Indore. His research mainly focuses on

studies of Thermonuclear X-ray Bursts on Neutron star

surface and its interaction with the Accretion disk and

Corona.

Part IV

Biosciences

Gene Knockout Strategies – The Scientiﬁc

Wrestle Of Gene Therapeutics

by Aengela Grace Jacob

airis4D, Vol.4, No.5, 2026

www.airis4d.com

1.1 Introduction

In the ﬁeld of genetic engineering and oncology,

researchers often need to silence a gene to understand

its function. This is typically achieved through

two primary strategies: Gene Knockout and Gene

Knockdown. While they sound similar, they operate on

diﬀerent biological levels and oﬀer unique advantages

for cancer research and therapy.Gene knockout involves

the complete permanent disruption of a gene at the DNA

level. By using genome-editing tools like CRISPR-

Cas9, the speciﬁc DNA sequence of a gene is deleted

or scrambled, ensuring that the cell can no longer

produce the corresponding protein. While considering

the case of gene knockdown, a temporary reduction in

gene expression, usually at the RNA level. Instead of

destroying the DNA, knockdown techniques intercept

the messenger RNA (mRNA) before it can be translated

into a protein.When a drug is developed (before the

initial process), researchers must prove a gene is a

viable target therefore by using knockdown or knockout

strategies in the lab, they can mimic the eﬀect of a

future drug. If knocking down Gene X stops a tumor

from spreading in a petri dish, Gene X becomes a high-

priority target for pharmaceutical development. Here

we would indulge in discussing on the gene knockout

methods and strategies and understand their upscaling

and leverage in therapeutic applications.

1.2 Fundamental Logic Of Knockout

The standard observational biology asks, What is

this gene doing? , knockout technology asks, What

happens to the system when this gene is gone? This

distinction is vital. It shifts the research from correlation

(observing that a gene is active during a process) to

causation (proving that the process cannot happen

without that gene).

The process typically targets the genomic DNA.

Unlike temporary methods that intercept messages, a

knockout creates a permanent, heritable change. When

a gene is knocked out, the cellular machinery can no

longer transcribe the DNA into functional messenger

RNA (mRNA), ensuring that the corresponding protein

is never synthesized.

There are certain primary methodologies

introduced to conduct the knockout procedures it

include homologous recombination (traditional method)

and ZFNs and TALENs and the gold standard CRISPR-

Cas9

1.3 Traditional Homologous

Recombination

Before the era of programmable nucleases,

scientists relied on a natural cellular repair process

called homologous recombination. Researchers would

introduce a DNA fragment containing a nonsense

sequence ﬂanked by sequences identical to the target

gene. Occasionally, the cell would mistakenly swap

1.7 Assisting Therapy

the functional gene for the broken one. While

groundbreaking (earning the Nobel Prize in 2007),

this was an ineﬃcient numbers game, often requiring

thousands of attempts to achieve a single successful

knockout.

1.4 The Programmable Nucleases

(ZFNs and TALENs)

To increase eﬃciency, scientists developed

molecular scissors that could be programmed to

cut speciﬁc DNA locations. Zinc-Finger Nucleases

(ZFNs)and TALENs were the ﬁrst to allow for targeted

double-strand breaks (DSBs). By forcing the cell to

repair a speciﬁc break, researchers could introduce

mutations that eﬀectively deactivated the gene.

1.5 CRISPR-Cas9: The Genetic

Revolution

The current gold standard is CRISPR-Cas9.

Derived from a bacterial immune system, CRISPR

uses a guide RNA to lead the Cas9 enzyme to a precise

coordinate in the genome. Once there, Cas9 snips the

DNA. When the cell attempts to repair this break using

a sloppy mechanism called Non-Homologous End

Joining (NHEJ) , it often introduces small insertions

or deletions (indels). These indels shift the reading

frame of the gene, creating a premature stop signal that

renders the gene completely non-functional.

1.6 Why We Need Gene Knockout?

This ﬁgure illustrates the CRISPR-Cas9 gene-

editing process, showing how a speciﬁc DNA sequence

is targeted, cut, and then repaired by the cell’s natural

mechanisms.

This is a very intriguing question to be pondered

upon so lets take an example, imagine the city starts

experiencing random ﬁres (tumors). We suspect a

speciﬁc oﬃce,let’s call it the Growth Oﬃce .It has a

broken manual that’s telling the city to build buildings

where they don’t belong.

Figure 1: This ﬁgure illustrates the CRISPR-Cas9 gene-

editing process, showing how a speciﬁc DNA sequence

is targeted, cut, and then repaired by the cell’s natural

mechanisms.

Image Courtesy: https://www.biorender.com/template

/crisprcas9-gene-editing

To prove this oﬃce is the problem, scientists use a

Knockout Strategy. If we completely delete the Growth

Oﬃce manual and the ﬁres stop, we have proven that

oﬃce was the culprit. Conversely, if we delete a Safety

Oﬃce manual and ﬁres suddenly start everywhere, we

realize that manual was actually a Tumor Suppressor -

a set of instructions meant to prevent ﬁres.

We can’t just throw a grenade into the library; we

need to be surgical. We use tools like CRISPR, which

acts like a biological GPS paired with a pair of scissors.

Now we need to know the factor of how,the answer is

through precision shredding;

The GPS (Guide RNA): We give the tool a

snippet of the manual we want to destroy. It

wanders through the library until it ﬁnds the

exact matching page.

The Scissors (Cas9): Once it ﬁnds the page, it

snips the DNA.

The Sloppy Repair: The cell is a neat freak; it

hates broken DNA. It tries to tape the page back

together immediately. But because it’s in such a

rush, it almost always makes a typo or leaves out

a sentence

The Result: That typo makes the manual

unreadable. The cell can no longer follow those

instructions. The gene is Knocked Out.

1.8 The Great Genetic Shuﬄe: A Symphony of Survival in Esophageal Cancer

Figure 2: This diagram illustrates how the CRISPR-

Cas9 system works to edit DNA

Image Courtesy: https://encrypted-tbn1.gstatic.com/licensed-image?q=tbn:ANd9GcR XMz75l w

Vf0fSb2L7 WC5L3KXmd5LkZJDWc3Qa8jgNwGQu70lLCgaTvEubv5ozeY5XcLiVO3LUnBW

WwfTI9vfGzhd9tFb8MDrbDdI5BSrDShQfM

1.7 Assisting Therapy

Now, we need to understand how this technique

helps to target the tumour which arise in our bodies.To

tackle this enemy we utilise the knockout strategy.By

knocking out genes one by one in cancer cells (a process

called a CRISPR screen), we can ﬁnd Achilles heels.

We might ﬁnd that while a cancer cell is aggressive, it

is uniquely dependent on one speciﬁc Utility Manual.

If we can ﬁnd a drug that mimics that knockout, we

can kill the cancer without hurting the healthy parts

of the body.In gene therapy, we actually use knockout

strategies to help our immune cells. Sometimes, our

immune cells (the city’s police) have a manual that tells

them, Don’t attack anything that looks like a citizen.

Cancer cells are clever and disguise themselves as

citizens. Scientists can knock out that speciﬁc Don’t

Attack instruction in the immune cells. Now, these

upgraded Bodyguards can see through the cancer’s

disguise and go on the oﬀensive. This is the CAR

T-cell Therapy, it is shown to be groundbreaking for

hematologic cancer treatments.

Figure 3: This image illustrates CAR-T cell therapy,

a specialized form of immunotherapy that genetically

engineers a patient’s own immune cells to recognize

and destroy cancer.

ImageCourtesy:https://encrypted-tbn2.gstatic.com/licensed-image?q=tbn:ANd9GcQDq4PfGy7r

dbeeD7WokUkPQ1ﬂVsoai0jdbSWFJz3DgDM Yn6cq9wuJFcnxqdWUWtYkNN6Cd4Qla54DRu

J owWVUpIu6MmyUjdOOaYKATsQMPmuxQ

1.8 The Great Genetic Shuﬄe: A

Symphony of Survival in

Esophageal Cancer

In the silent, cellular theatre of the Esophageal

Adenocarcinoma (EAC), a complex and deadly

transformation unfolds. The recent study published

in Cell Death and Disease reveals that the progression

of this cancer from its precursor, Barrett’s Esophagus

(BE), to a lethal malignancy is orchestrated not just

by mutations, but by a subtle genetic sleight of hand

known as isoform switching (IS).

1.9 The Shifting Genetic Mask

Think of a gene as a master blueprint. Through

alternative splicing, a single gene can produce diﬀerent

versions of its protein output, called isoforms. The

researchers discovered that as EAC develops, the cell

switches its preference from one isoform to another. By

analyzing RNA-sequencing data, the team identiﬁed:

71 genes that underwent signiﬁcant isoform switching

during the progression to EAC. 42 speciﬁc isoforms

that serve as grim harbingers, directly linked to all-

1.12 A New Horizon for Treatment

cause patient mortality. A unique synergy between

these switches and the status of TP53, the most

commonly mutated gene in this cancer type.

The Protagonists of Pathogenesis: TTLL12 and

HM13: To bridge the gap between observation and

action, the study focused on two high-stakes players

identiﬁed by survival analysis: TTLL12 and HM13.

1.10 The TTLL12 Gambit: Evading

the Quality Control

The coding isoform TTLL12-201 emerges as a

villain in the EAC narrative. When researchers silenced

this speciﬁc isoform using siRNA, the results were

transformative: Activation of Chaperone-Mediated

Autophagy (CMA): Knocking down TTLL12-201

forced the cell to restart its internal cleaning system.

Degradation of CHK1 and TP53: This activated

autophagy targeted and degraded key survival proteins

like CHK1, which normally helps the cancer repair

its DNA. Chemosensitivity: Silencing this isoform

sensitized cancer cells to standard chemotherapy

(paclitaxel and carboplatin), turning a resistant foe

into a vulnerable one.

1.11 The HM13 Maneuver: Stressing

the System

The HM13-201 isoform acts as a shield within

the Endoplasmic Reticulum (ER). Targeting it triggered

a cellular catastrophe: Unfolded Protein Response

(UPR): Its removal induced massive ER stress,

overwhelming the cancer cell with misfolded protein

garbage. Translation Arrest and Apoptosis: The cell

eﬀectively stopped producing new proteins and initiated

programmed cell death (apoptosis). Immunotherapy

Synergy: Remarkably, silencing HM13 increased the

response to avelumab , an anti-PD-L1 agent, suggesting

that this genetic switch plays a role in helping the cancer

hide from the immune system.

1.12 A New Horizon for Treatment

Perhaps the most promising ﬁnding is the

selectivity of these targets. While silencing TTLL12

and HM13 was devastating to EAC cells, it had minimal

eﬀect on normal esophageal cells (Het-1A), oﬀering a

goldilocks opportunity for precision medicine with low

toxicity.

By moving beyond simple mutations and looking

at the isoform landscape, this research provides a new

map for prognostic markers and therapeutic targets. It

suggests that the future of EAC treatment may lie in

reversing these genetic switches, forcing the cancer to

unmask itself and face the cellular consequences.

REFERENCES

https://www.nature.com/articles/s41392-023-0

1309-7

https://www.nature.com/articles/s41392-025-0

2269-w

https://www.cancer.gov/about-cancer/treatmen

t/research/car-t-cells

https://www.nature.com/articles/s41598-025-2

4184-4

https://link.springer.com/article/10.1038/s414

19-026-08542-2

About the Author

Aengela Grace Jacob is Final Year Student

of Bsc Biotechnology , Chemistry ( BSc BtC ) Dual

Major at Christ University Central campus, Bangalore

The eDNA Metabarcoding Model:

Next-Generation Biodiversity Assessment -

Part 2

by Geetha Paul

airis4D, Vol.4, No.5, 2026

www.airis4d.com

2.1 Introduction:

The Molecular Revolution in Ecology

For centuries, biodiversity monitoring has relied

on traditional methods such as nets, traps, and visual

surveys, techniques that, while foundational, are

labour-intensive, taxonomically limited, and often

invasive. As the global biodiversity crisis accelerates,

driven by habitat loss, climate change, and species

extinctions, the need for faster, more comprehensive,

and non-destructive monitoring tools has become urgent.

Enter environmental DNA (eDNA) metabarcoding,

a revolutionary approach that shifts the focus from

direct organismal observation to the detection of the

genetic traces species leave behind in their surroundings.

Every organism sheds DNA through skin cells, mucus,

faeces, or gametes into water, soil, or air. By

capturing and sequencing these genetic fragments using

high-throughput sequencing (HTS), researchers can

identify entire communities from a single sample,

uncovering rare, cryptic, or elusive species that

traditional methods miss. This technique oﬀers

unparalleled advantages: high sensitivity, scalability,

non-invasiveness, and the ability to process hundreds

of samples simultaneously. From tracking endangered

species and invasive organisms to reconstructing ancient

ecosystems and monitoring microbial communities,

DNA metabarcoding is transforming conservation

biology, ecological research, and environmental policy.

Yet challenges remain, including gaps in reference

databases, PCR biases, and the need for standardised

protocols. As technology advances with portable

sequencers, machine learning, and global DNA

databases, metabarcoding is poised to become the

cornerstone of biodiversity assessment, ushering in

a new era in which ecosystems are decoded not by sight

but by their genetic ﬁngerprints.

2.2 The Theoretical Framework:

From Cells to Sequences

The eDNA metabarcoding model operates on

a multi-stage pipeline that integrates ﬁeld biology,

molecular genetics, and advanced bioinformatics.

Environmental Shedding and Persistence: The

model begins with the biological reality that

organisms lose DNA through faeces, mucous,

gametes, and decaying tissue. The detectability

of a species depends on the rate of shedding

versus the rate of degradation. In aquatic systems,

factors such as UV radiation, pH, and temperature

dictate how long this genetic memory persists,

usually ranging from a few days to several weeks.

The Power of Universal Primers: Unlike

traditional DNA barcoding, which targets a single

specimen, metabarcoding uses universal primers.

These are short DNA sequences designed to

2.4 Strategic Advantages and the Biotic Index

Figure 1: Sampling of River water for DNA extraction

to identify species.

Image courtesy: https://www.bing.com/images/search?view=detailV2&ccid=NgDBPWbw&id=06

8A58357A8DCBEF2E98EFDA061BD6869C253946&thid

bind to highly conserved regions across a broad

group of taxa (e.g., all ﬁsh or all insects), while

ﬂanking a variable region speciﬁc to each species.

The Cytochrome c Oxidase subunit I (COI)

gene is the most common target for animal DNA

barcoding, often referred to as the biological

barcode.

2.3 The Technical Pipeline: Field to

Data

Implementing the eDNA model requires a rigorous

technical workﬂow to ensure that the data accurately

reﬂects the biological reality of the site.

Phase I: Capture and Extraction

Sampling typically involves ﬁltering large volumes

of water (often 1–5 litres) through ﬁne-pore membranes

to capture DNA fragments. Because eDNA is

highly sensitive, preventing contamination is the most

signiﬁcant challenge. Once captured, the DNA is

extracted in a sterile laboratory environment using

specialised kits designed to remove environmental

inhibitors, such as humic acids, which can interfere

with subsequent chemical reactions.

Phase II: Library Preparation and Sequencing

The extracted DNA undergoes Polymerase Chain

Reaction (PCR) ampliﬁcation. During this stage,

unique indexes or molecular tags are attached to the

DNA from each sample, allowing multiple samples to

be pooled together in a single sequencing run. The

library is then sequenced using platforms like Illumina,

which generate millions of individual reads.

Figure 2: The extracted DNA undergoes Polymerase

Chain Reaction (PCR) ampliﬁcation.

Image courtesy:pcr factsheet.jpg (1800×1800

Phase III: Bioinformatics and Taxonomy

The resulting raw data is a massive digital

haystack of sequences. Bioinformatics pipelines

(such as QIIME2 or OBITools) ﬁlter out noise and

errors. These sequences are then clustered into

Operational Taxonomic Units (OTUs) or mapped

to global reference databases such as GenBank or the

Barcode of Life Data System (BOLD). The result is a

detailed list of every detectable species present in the

original environment.

2.4 Strategic Advantages and the

Biotic Index

The eDNA metabarcoding model oﬀers several

distinct advantages over traditional morphological

surveys:

Detection of Rare and Invasive Species: eDNA

is exceptionally sensitive. It can detect the

presence of a single invasive carp in a massive

lake or a rare, endangered dragonﬂy nymph in

a remote stream long before a human observer

would ﬁnd them.

Cost and Time Eﬃciency: A single technician

can collect dozens of water samples in a day,

covering an area that would take a team of

taxonomists weeks to survey using traditional

2.5 Challenges: The Road to Standardisation

Figure 3: Schematic diagram of eDNA sample

collection, analysis, and functions.

Image courtesy:Environmental DNA (eDNA) Technology in Biodiversity and Ecosystem Health

Research: Advances and Prospects - Wu - 2026 - Ecology and Evolution - Wiley Online Library

nets and microscopes.

Non-Invasive Monitoring: This is the gold

standard for conservation in protected areas. It

allows monitoring biodiversity without capturing,

stressing, or killing specimens.

The model is being used to develop a Molecular

Biotic Index. By analysing the entire macroinvertebrate

community via eDNA, researchers can assign a health

score to a watershed. Certain species are highly

sensitive to pollution; their presence, detected via their

DNA, serves as a real-time indicator of water quality

and ecosystem resilience.

2.5 Challenges: The Road to

Standardisation

Irrespective of its potential, the eDNA model faces

hurdles that require ongoing research.

The Reference Database Gap: A DNA

sequence is only useful if it can be matched

to a known species. In many tropical regions and

among speciﬁc groups like Odonata (dragonﬂies),

reference libraries are still being built.

Primer Bias: Not all DNA sequences amplify

with equal eﬃciency. Some species may be

hidden if the universal primers do not bind

perfectly to their genetic code, leading to under-

representation.

Quantiﬁcation: While eDNA tells us who is

there, it is still diﬃcult to determine exactly

how many individuals are present. While

sequence read counts often correlate with

biomass, environmental variables complicate

absolute quantiﬁcation.

Conclusion:

The Future of Ecological Surveillance

The eDNA metabarcoding model represents the

ultimate synthesis of technology and natural history.

As sequencing costs continue to fall and reference

databases expand, this model will become the standard

for environmental impact assessments, biosecurity, and

climate change monitoring.

By looking into a drop of water and seeing

the genetic signatures of an entire forest or river

system, we gain a more profound understanding of

the interconnectedness of life. For the next generation

of researchers, the ability to decode these environmental

secrets will be the key to preserving the earth”s most

vulnerable ecosystems, from the depths of the oceans

to the peaks of the Western Ghats.

Reference

eDNAmanual-eng ver3 0 0.pdf

Sato, Y., Miya, M., Fukunaga, T., Sado, T.

& Iwasaki, W. 2018. “MitoFish and MiFish

Pipeline: A Mitochondrial Genome Database of Fish

with an Analysis Pipeline for Environmental DNA

Metabarcoding”. Molecular Biology and Evolution

35:1553-1555.

Fukuzawa, T., Shirakura, H., Nishizawa, N.,

Nagata, H., Kameda, Y., & Doi, H. 2023,

“Environmental DNA extraction method from water

for a high and consistent DNA yield.” Environmental

DNA 5 (4): 627–633. doi: 10.1002/edn3.406

Uchii, K., Doi, H. & Minamoto, T. 2016, “A novel

environmental DNA approach to quantify the cryptic

invasion of non-native genotypes.” Molecular Ecology

Resources 16 (2): 415-422. Doi: 10.1111/1755-

0998.12460

Wong, M. K. S, Nakao, M. & Hyodo, S.

2020, “Field application of an improved protocol

for environmental DNA extraction, puriﬁcation, and

2.5 Challenges: The Road to Standardisation

measurement using Sterivex ﬁlter.” Scientiﬁc Reports

10: 21531. doi: 10.1038/s41598-020-77304-7

About the Author

Dr. Geetha Paul is one of the directors of

airis4D. She leads the Biosciences Division. Her

research interests extends from Cell & Molecular

Biology to Environmental Sciences, Odonatology, and

Aquatic Biology.

About airis4D

Artiﬁcial Intelligence Research and Intelligent Systems (airis4D) is an AI and Bio-sciences Research Centre.

The Centre aims to create new knowledge in the ﬁeld of Space Science, Astronomy, Robotics, Agri Science,

Industry, and Biodiversity to bring Progress and Plenitude to the People and the Planet.

Vision

Humanity is in the 4th Industrial Revolution era, which operates on a cyber-physical production system. Cutting-

edge research and development in science and technology to create new knowledge and skills become the key to

the new world economy. Most of the resources for this goal can be harnessed by integrating biological systems

with intelligent computing systems oﬀered by AI. The future survival of humans, animals, and the ecosystem

depends on how eﬃciently the realities and resources are responsibly used for abundance and wellness. Artiﬁcial

intelligence Research and Intelligent Systems pursue this vision and look for the best actions that ensure an

abundant environment and ecosystem for the planet and the people.

Mission Statement

The 4D in airis4D represents the mission to Dream, Design, Develop, and Deploy Knowledge with the ﬁre of

commitment and dedication towards humanity and the ecosystem.

Dream

To promote the unlimited human potential to dream the impossible.

Design

To nurture the human capacity to articulate a dream and logically realise it.

Develop

To assist the talents to materialise a design into a product, a service, a knowledge that beneﬁts the community

and the planet.

Deploy

To realise and educate humanity that a knowledge that is not deployed makes no diﬀerence by its absence.

Campus

Situated in a lush green village campus in Thelliyoor, Kerala, India, airis4D was established under the auspicious

of SEED Foundation (Susthiratha, Environment, Education Development Foundation) a not-for-proﬁt company

for promoting Education, Research. Engineering, Biology, Development, etc.

The whole campus is powered by Solar power and has a rain harvesting facility to provide suﬃcient water supply

for up to three months of drought. The computing facility in the campus is accessible from anywhere through a

dedicated optical ﬁbre internet connectivity 24×7.

There is a freshwater stream that originates from the nearby hills and ﬂows through the middle of the campus.

The campus is a noted habitat for the biodiversity of tropical Fauna and Flora. airis4D carry out periodic and

systematic water quality and species diversity surveys in the region to ensure its richness. It is our pride that the

site has consistently been environment-friendly and rich in biodiversity. airis4D is also growing fruit plants that

can feed birds and provide water bodies to survive the drought.