Cover page

Image: Prof Dr P P Divakaran : Scientist, Historian, Truth-Seeker says MG Radhakrishnan in his article that

appeared in Mathrubhumi. The photograph is from the same article. The article was written remembering Prof

Divakaran (1936–2025) who was a a respected physicist and the ﬁnest multi-talented humane personalities one

may come across. “He was an authority everybody sort of looked up to. A person who actually had the domain

knowledge, and he was able to shed the correct light on the historical part,” Kaushal Verma, Dean, Physical &

Mathematical Sciences Division, Indian Institute of Science (IISc), quoted The Print. Above all, he was a good

friend, a well-wisher of airis4D at all times. We are deeply moved by his passing away. This edition of airis4D

pays homage to his memory. Photo: adopted from Mathrubhumi

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.3, No.9, 2025

www.airis4d.com

This edition of Journal airis4D is an obituary

for Prof. Dr. P. P. Divakaran, a renowned physicist,

historian, and truth-seeker, who passed away on 23rd

August 2025, leaving behind a legacy of scholarship and

humanity. He was admired not only for his authority in

physics but also for his clarity in illuminating history.

Beyond his brilliance, Prof. Divakaran was a good

friend and a constant well-wisher of airis4D. His passing

deeply moves us, and with this edition, we pay homage

to his enduring memory.

Jinsu Ann Mathew’s article Entropy in Networks:

Order, Uncertainty, and the Dynamics of Connection

explores how entropy—a concept from physics and

information theory—helps us understand the complexity

of networks. It introduces three key structural measures

(degree distribution entropy, spectral entropy, and

topological entropy) to quantify connectivity and

organisation, and extends to dynamic entropy, which

captures how information, resources, or diseases ﬂow

through networks. Across domains like communication,

biology, epidemiology, and social systems, entropy

reveals the balance between order and unpredictability,

eﬃciency and resilience, showing networks as dynamic

systems where stability and adaptability must coexist.

Abishek P. S.’s article Plasma Physics Solar

Physics highlights how plasma—making up over 99%

of visible matter—governs the Sun’s dynamics through

ionisation, magnetohydrodynamics, the solar dynamo,

and wave–particle interactions. Using tools like

helioseismology, spectroscopy, and space missions

(Parker Solar Probe, SDO), scientists study solar ﬂares,

CMEs, and the solar wind that shape space weather and

aﬀect Earth. Beyond astrophysics, this research informs

fusion energy, satellite protection, and advances our

understanding of both cosmic processes and practical

technologies.

Ajit Kembhavi’s Black Hole Stories–21 recounts

the historic ﬁrst detection of gravitational waves by

LIGO on 14 September 2015. The signal, GW150914,

came from the merger of two black holes—36 and 29

solar masses—1.3 billion light-years away, forming a

62-solar-mass Kerr black hole. In less than a second,

energy equivalent to three solar masses was radiated as

gravitational waves, conﬁrming Einstein’s century-old

prediction and opening a new window to the Universe.

Aromal P’s article X-ray Astronomy: Through

Missions surveys major X-ray space observatories of

the late 2010s. It highlights NASA’s NICER (pioneering

precise neutron star measurements), China’s Insight-

HXMT (advancing black hole and pulsar studies),

and the Russian–German Spektr-RG mission (mapping

the X-ray universe and discovering thousands of new

galaxy clusters). Together, these missions mark a

transformative era in high-energy astrophysics.

Sindhu G’s Isochrones in Astronomy explains

how isochrones—curves on the Hertzsprung–Russell

diagram representing stars of the same age—are

constructed from stellar evolution models and applied

to date star clusters, measure distances, study galactic

history, and characterise exoplanet hosts. Despite

challenges from stellar physics uncertainties, binaries,

and dust eﬀects, advances from Gaia, improved models,

and machine learning promise more precise isochrone

applications in future astrophysics.

Geetha Paul’s The Symphony of Signal explores

neurotransmission—the precise electrical and chemical

dialogue between neurons that underlies thought,

movement, emotion, and memory. The article explains

how neurotransmitters like dopamine, serotonin, and

acetylcholine are synthesised, released, and inactivated,

highlighting their roles in health, disease, and therapy.

iii

Contents

Editorial ii

I Artiﬁcial Intelligence and Machine Learning 1

1 Entropy in Networks: Order, Uncertainty, and the Dynamics of Connection 2

1.1 Entropy in Network Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Entropy in Network Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Applications Across Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

II Astronomy and Astrophysics 6

1 Plasma Physics & Solar Physics 7

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.2 Plasma State and Ionization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Magnetohydrodynamics (MHD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4 Solar dynamo theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.5 Wave-Particle Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.6 Solar Wind and Heliosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.7 Observational and experimental tools in Solar Physics . . . . . . . . . . . . . . . . . . . . . . . . 10

1.8 Relevance of Solar Plasma Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Black Hole Stories-21

The First LIGO Detection – A Binary Black Hole 13

2.1 The First Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 The Nature of GW150914 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3 A Model for the Black Hole Binary GW150914 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 X-ray Astronomy: Through Missions 17

3.1 Satellites in 2015s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 NICER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 Insight-HXMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.4 Spektr-RG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Isochrones in Astronomy 20

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 Theoretical Basis of Isochrones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.3 Construction of Isochrones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.4 Applications of Isochrones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.5 Challenges and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

CONTENTS

4.6 Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

III Biosciences 23

1 The Symphony of Signal: A Deep Dive into Neurotransmission 24

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.2 Steps involved in the process of Neurotransmission . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.3 Monoamine neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.4 Catechol-O-methyltransferase (COMT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.5 Acetylcholine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Part I

Artiﬁcial Intelligence and Machine Learning

Entropy in Networks: Order, Uncertainty, and

the Dynamics of Connection

by Jinsu Ann Mathew

airis4D, Vol.3, No.9, 2025

www.airis4d.com

In earlier articles, we explored how entropy, ﬁrst

rooted in physics and information theory, provides

a measure of uncertainty and disorder in physical

systems. We then followed entropy into the world

of natural language processing, where it reveals the

hidden structure of texts through lexical diversity, n-

gram entropy, and cross-entropy in language models.

Extending this journey, we examined human language

itself, showing how ambiguity, redundancy, and

predictability can all be understood as manifestations

of entropy in communication.

This article continues that exploration by moving

from individual systems—whether physical states,

sentences, or utterances—to collective systems. These

collective systems take the form of networks, which are

everywhere: in the neurons of the brain, the ties of social

media, the nodes of the internet, and the interactions

of ecosystems and economies. Networks represent the

fabric of relationships that deﬁne complexity in nature

and technology. By applying entropy to networks, we

gain a powerful lens to measure structural complexity,

dynamic uncertainty, and resilience.

1.1 Entropy in Network Structure

The structure of a network is determined by

how its nodes and edges are arranged. While we

can often “see” a network’s complexity when drawn,

entropy provides a way to quantify that complexity

mathematically. By treating diﬀerent aspects of the

network as probability distributions, we can measure

how much uncertainty or unpredictability is embedded

in its structure. Three of the most widely discussed

notions are degree distribution entropy, spectral entropy,

and topological entropy.

Degree Distribution Entropy

Degree distribution entropy is perhaps the most

intuitive. Every node in a network has a degree, meaning

the number of connections it maintains. If we look at

all nodes together, we obtain a degree distribution: the

probability that a randomly chosen node has a certain

degree. The entropy of this distribution tells us how

diverse and unpredictable the network’s connectivity

is. Consider a star-shaped network, like an airline hub

system where one airport connects to every other but

all other airports only connect to the hub. Such a

system is extremely predictable: the hub is always the

center of traﬃc. The entropy here is low because the

degree distribution is sharply uneven. Contrast this

with a friendship network in a classroom, where some

students are very social, others less so, and many fall

in between. This diversity of connections increases

uncertainty about which degree a randomly chosen

node might have, leading to higher entropy. In short,

degree distribution entropy reﬂects how egalitarian or

concentrated a network’s connections are.

Spectral Entropy

Another way to capture the complexity of a network

is by looking at its spectral properties. A network can

1.2 Entropy in Network Dynamics

be represented by an adjacency matrix, which records

which nodes are connected to which. From this matrix,

we can calculate eigenvalues, which reﬂect the overall

connectivity patterns of the network. The distribution of

these eigenvalues can then be treated like a probability

distribution, allowing us to compute entropy. This

measure, called spectral entropy, quantiﬁes how ordered

or disordered the network’s structure is.

Imagine a perfectly regular crystal lattice in

physics, where each atom connects to its neighbors

in a ﬁxed and repeating pattern. The eigenvalues of

such a highly regular network are very structured and

predictable, and thus the spectral entropy is low. Now

compare this to a biological neural network, where the

connections between neurons are far more irregular and

variable. The eigenvalue spectrum here is more spread

out and less predictable, leading to higher spectral

entropy.

In other words, spectral entropy gives us a global

view of how “organized” or “messy” the overall network

is. Where degree distribution focuses only on how

evenly connections are spread across nodes, spectral

entropy reﬂects whether the entire system behaves like

a predictable lattice or a disordered web.

Topological Entropy

The third perspective is topological entropy, which

considers how complexity unfolds as one moves through

a network. If you imagine walking along the nodes

of a chain, like beads on a string, the path is entirely

predictable—you can only move forward or backward.

The growth of possible paths is minimal, and so the

entropy is low. By contrast, imagine browsing the

internet through hyperlinks, or exploring a dense web

of acquaintances in a social network. At each step,

the number of possible next moves grows explosively,

creating unpredictability about where you might end up.

This rapid expansion of possibilities is reﬂected in a

high topological entropy. Topological entropy therefore

measures the richness of navigational or dynamical

complexity within the network.

Taken together, these three notions—degree

distribution entropy, spectral entropy, and topological

entropy—paint a comprehensive picture of network

structure. Degree distribution captures the fairness of

connectivity, spectral entropy reveals global structural

regularities or irregularities, and topological entropy

measures the branching richness of possible paths. By

applying these measures, researchers can move beyond

visual impressions of networks and develop a rigorous

language for describing and comparing the complexity

of systems as diverse as airline routes, neural circuits,

city layouts, and online social platforms.

1.2 Entropy in Network Dynamics

While structural entropy focuses on how a network

is built, dynamic entropy captures how processes unfold

on top of that structure. Networks are not static: they

carry information, resources, or signals across their

nodes. Entropy provides a natural way to measure

the uncertainty, unpredictability, and diversity of these

ﬂows. By studying entropy in network dynamics, we

can better understand how robust, eﬃcient, or fragile a

system is in motion.

Information Flow Entropy

One of the most important ideas is information

ﬂow entropy. In many networks—whether it be

messages traveling across the internet, rumors spreading

through social media, or impulses moving between

neurons—information can follow multiple routes. If

there is only a single, predictable path for transmission,

the entropy is low. For example, in a centralized

communication network, all messages may pass through

one central server, so the ﬂow is highly constrained

and predictable. By contrast, in a peer-to-peer ﬁle-

sharing network, there may be dozens of possible

routes for a message to reach its destination. This

diversity of possible paths increases uncertainty, giving

the system higher entropy. High ﬂow entropy can make

communication more resilient—if one path fails, others

remain—but it also makes the ﬂow less predictable.

1.3 Applications Across Domains

Robustness and Vulnerability

Entropy in network dynamics is also closely tied

to robustness and vulnerability. Networks that are too

orderly may function eﬃciently under normal conditions

but are fragile when disrupted. For instance, in a power

grid designed around a small number of central hubs,

failure of a single hub can trigger cascading breakdowns.

Such a system has low entropy because its dynamics are

highly concentrated around predictable routes. On the

other hand, a system with very high entropy—where

signals or resources disperse unpredictably—may lack

stability and coherence. Interestingly, many natural and

human-made networks seem to operate in a balance,

maintaining a moderate level of entropy that allows both

resilience and eﬃciency. This balance helps explain

why the internet, biological ecosystems, and social

networks often survive shocks yet remain functional.

Predictability of Spreading Processes

Another dimension of dynamic entropy involves

the predictability of spreading processes. Consider the

example of an epidemic spreading through a community.

If the social network is highly clustered, the disease may

spread along a few well-deﬁned routes, resulting in low

entropy of infection pathways. If the network is more

loosely structured, the disease may jump across many

unpredictable links, leading to high entropy. Similarly,

in online environments, a viral meme might follow either

a few predictable inﬂuencers (low entropy spread) or

leap across the network through diverse routes (high

entropy spread). In both cases, entropy serves as a

measure of how predictable or surprising the spread is.

In summary, entropy in network dynamics captures

the uncertainty of motion and ﬂow within a system.

It tells us whether communication is centralized or

distributed, whether processes are robust or fragile, and

whether spreading events are predictable or chaotic. Just

as structural entropy gives us a snapshot of the network’s

architecture, dynamic entropy provides a moving picture

of how that architecture is used. Together, they form

a comprehensive framework for understanding the

complexity of networks in action.

1.3 Applications Across Domains

The idea of using entropy to study networks is not

just abstract mathematics—it has powerful applications

across many ﬁelds of science and society. By measuring

uncertainty and unpredictability in how networks are

structured and how processes ﬂow through them,

researchers can uncover hidden patterns, assess stability,

and even predict failures.

Communication Networks

In communication networks, entropy helps

describe how information spreads across the internet or

through social media platforms. A centralized system,

where most traﬃc ﬂows through a handful of servers,

exhibits low entropy: the paths are predictable but

also vulnerable to attack or overload. In contrast,

decentralized peer-to-peer networks have higher entropy,

as messages can take many diﬀerent routes. This

diversity makes them harder to shut down and more

resilient, but it also introduces unpredictability in

delivery times. Studying entropy in these systems gives

engineers a way to balance eﬃciency and robustness.

Biology and Neuroscience

In biology and neuroscience, entropy provides

a lens for understanding complex living systems.

Neural networks in the brain, for instance, display rich

dynamics that can be studied with entropy. Low-entropy

states often correspond to deep sleep, where brain

activity is highly synchronized and predictable. High-

entropy states, in contrast, are linked to wakefulness

and consciousness, when neural signals are diverse

and less predictable. Similar approaches have been

used in genetics and systems biology, where entropy

measures help describe how robust a metabolic or

protein-interaction network is against perturbations.

Epidemiology

In epidemiology, entropy in network dynamics

oﬀers a way to study how diseases spread. A community

where most people interact within tight, predictable

clusters has low entropy: infections move along narrow,

1.4 Conclusion

well-deﬁned routes. But when connections are more

varied and unpredictable—such as in large, highly

mobile urban populations—the entropy of disease

spread increases, making outbreaks harder to trace

and control. Public health models often rely on

entropy-based measures to evaluate the eﬀectiveness

of interventions like travel restrictions or vaccination

strategies.

Social Systems

Finally, in social systems, entropy sheds light

on how ideas, rumors, or cultural trends spread. A

tightly controlled ﬂow of information, such as in a

hierarchical organization, has low entropy—messages

follow predictable paths, but innovation is limited. In

open social networks, where anyone can connect with

anyone else, entropy is higher, leading to more diverse

and unpredictable exchanges. This can foster creativity

but also allows misinformation to spread rapidly.

1.4 Conclusion

In our earlier explorations, entropy appeared ﬁrst

as a bridge between physics and information theory, then

as a measure of uncertainty in language and meaning.

With networks, we encounter a new dimension: entropy

no longer measures just the randomness of states or

symbols, but the unpredictability of connections and

ﬂows. Whether in the structure of a network—captured

by degree distributions or spectral measures—or in the

dynamics of how processes evolve across it, entropy

provides a unifying lens for complexity.

This perspective shows that networks are not

merely collections of nodes and edges, but dynamic

systems where predictability and uncertainty coexist.

Low entropy can signal stability but also fragility,

while high entropy may reﬂect adaptability at the cost

of control. By quantifying these trade-oﬀs, entropy

allows us to compare systems as diverse as the brain,

the internet, economies, and cities within a single

conceptual framework.

Perhaps the most striking lesson is the universality

of entropy. From particles to words to networks, the

same principle recurs: uncertainty is not noise to be

discarded, but a feature to be measured, understood,

and even harnessed. Each domain reveals new faces

of entropy, and together they point toward a broader

philosophy of complexity—one in which randomness

and order are deeply intertwined.

As we continue this journey, the study of entropy

in networks stands as a reminder that complexity is not

chaos, but structured unpredictability. In embracing

this view, we open the door to deeper insights into the

systems that shape both nature and society.

References

Degree-Based Graph Entropy in

Structure–Property Modeling

Information processing in complex networks:

Graph entropy and information functionals

A history of graph entropy measures

On graph entropy measures based on the number

of independent sets and matchings

Spectral Entropy – An Underestimated Time

Series Feature

Chemical Graph Theory—The Mathematical

Connection

Degree-Based Entropy of Some Classes of

Networks

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 II

Astronomy and Astrophysics

Plasma Physics & Solar Physics

by Abishek P S

airis4D, Vol.3, No.9, 2025

www.airis4d.com

1.1 Introduction

The study of plasma physics and solar physics

oﬀers a profound gateway into understanding the

fundamental processes that govern not only our Sun but

much of the observable universe. Plasma physics, which

deals with the behaviour of ionized gases, is essential

because over 99% of the visible matter in the cosmos

exists in this state[1]. In the context of solar physics,

plasma becomes the medium through which energy,

momentum, and magnetic ﬁelds interact in complex

and often turbulent ways. The Sun, our closest star,

serves as a natural laboratory for plasma phenomena,

exhibiting dynamic behaviours such as solar ﬂares,

coronal mass ejections, and the solar wind-all driven by

the interplay of magnetic ﬁelds and charged particles.

Through the lens of magnetohydrodynamics (MHD),

scientists model the Sun’s interior and atmosphere,

exploring how ﬂuid-like plasma ﬂows are shaped

by electromagnetic forces. Observational tools like

helioseismology, spectropolarimetry, and space-based

missions such as the Parker Solar Probe and Solar

Orbiter provide critical data that help validate theoretical

models[2,3]and uncover new layers of complexity.

Describing the Sun as a plasma laboratory is

not merely metaphorical-it reﬂects the reality that

our star oﬀers a naturally occurring, high-energy

environment where the principles of plasma physics

can be observed and tested at scales unattainable on

Earth. The Sun is composed almost entirely of plasma,

allowing it to conduct electricity and interact strongly

with magnetic ﬁelds. This plasma is not static; it is

governed by a complex interplay of electromagnetic

forces, ﬂuid dynamics, and nuclear reactions. At its

core, nuclear fusion converts hydrogen into helium,

releasing vast amounts of energy that propagate outward

through radiative and convective zones. As this energy

reaches the outer layers-the photosphere, chromosphere,

and corona-it drives dynamic phenomena such as

solar ﬂares, coronal mass ejections, and the solar

wind. These events are shaped by magnetic ﬁeld

lines that twist, reconnect, and evolve due to the

Sun’s diﬀerential rotation and turbulent convection,

processes best described by magnetohydrodynamics

(MHD) [4]. Plasma physics provides the theoretical

and computational tools to model these behaviours,

while observational instruments like spectrometers,

helioseismology arrays, and space-based probes oﬀer

empirical data to validate and reﬁne our understanding.

In essence, the Sun serves as a vast, self-sustaining

plasma experiment, enabling researchers to study

fundamental processes that inﬂuence not only stellar

evolution but also planetary environments and space

weather throughout the solar system..

1.2 Plasma State and Ionization

In the solar context, the plasma state and ionization

represent the most fundamental aspects of solar physics.

At the extreme temperatures found within the Sun-

ranging from approximately 6,000 K at the photosphere

to over 15 million K in the core-thermal energy is

suﬃcient to strip electrons from atoms, resulting in a

fully ionized gas composed of free electrons and atomic

nuclei. This ionized state, known as plasma, dominates

the Sun’s composition and behaviour. Unlike neutral

1.3 Magnetohydrodynamics (MHD)

gases, plasma is electrically conductive and highly

responsive to magnetic and electric ﬁelds, which makes

it a dynamic medium for energy transport and magnetic

interactions. To quantify the degree of ionization

under varying solar conditions, we apply the Saha

ionization equation, a thermodynamic relation that links

ionization levels to temperature and particle density[5].

This equation is particularly useful in modelling the

transition zones of the Sun, such as the chromosphere

and photosphere, where partial ionization occurs and

radiative processes are complex. The prevalence

of plasma throughout the Sun-from its core to the

outer corona-forms the basis for understanding solar

phenomena like ﬂares, prominences, and the solar wind.

Thus, the plasma state is not just a descriptive label but

a critical framework for interpreting the Sun’s structure,

dynamics, and its inﬂuence on the heliosphere

1.3 Magnetohydrodynamics (MHD)

Magnetohydrodynamics (MHD) is a cornerstone

of solar physics, oﬀering a powerful theoretical

framework to understand the behaviour of the

Sun’s plasma[6]. MHD merges the principles

of ﬂuid dynamics with Maxwell’s equations of

electromagnetism to describe how electrically

conductive ﬂuids-like the ionized plasma in the Sun-

interact with magnetic ﬁelds. One of the most

critical features of solar plasma is its high electrical

conductivity, which leads to the phenomenon known

as the “frozen-in” condition: magnetic ﬁeld lines are

eﬀectively locked into the plasma and move along with

it. This coupling between plasma motion and magnetic

ﬁeld evolution is central to many solar phenomena.

MHD equations allow modelling and prediction

of dynamic solar events such as solar ﬂares and coronal

mass ejections (CMEs), which are sudden releases

of magnetic energy that accelerate particles and eject

massive amounts of plasma into space[7]. These events

are often triggered by magnetic reconnection, a process

where oppositely directed magnetic ﬁeld lines break and

reconnect, releasing stored magnetic energy explosively.

MHD also explains the formation and evolution of

sunspots, which are regions of intense magnetic activity

Figure 1: Parker Solar Probe

Image courtesy: https://science.nasa.gov/mission/parker-solar-probe/

on the solar surface, and their role in the 11-year solar

cycle, during which the Sun’s magnetic ﬁeld reverses

polarity and solar activity waxes and wanes.

Through numerical simulations and observational

data from missions like the Solar Dynamics Observatory

(SDO) and Parker Solar Probe, scientists reﬁne MHD

models to better understand the Sun’s magnetic

architecture and its inﬂuence on the heliosphere[2].

In essence, MHD is not just a theoretical tool-it is the

language through which scientists decode the Sun’s

most energetic and enigmatic behaviours

1.4 Solar dynamo theory

The solar dynamo theory is central to

understanding the origin and evolution of the Sun’s

magnetic ﬁeld[8,9]. This theory posits that the

magnetic ﬁeld is generated and sustained by a dynamo

mechanism operating within the Sun’s convective zone,

where hot plasma rises and cooler plasma sinks in

a turbulent, churning motion. Crucially, the Sun

exhibits diﬀerential rotation-its equator rotates faster

than its poles-which stretches and twists magnetic ﬁeld

lines over time. Combined with helical turbulence

from convective motions, this diﬀerential rotation

ampliﬁes and reorganizes magnetic ﬁelds through a

process known as the

–

Ω

dynamo. The result is

a self-sustaining cycle of magnetic ﬁeld generation

that manifests in the 11-year solar cycle, during which

the Sun’s magnetic polarity reverses and solar activity

ﬂuctuates.

This dynamo action explains the periodic

emergence of sunspots, regions of intense magnetic

1.5 Wave-Particle Interactions

concentration, and the occurrence of solar ﬂares and

coronal mass ejections (CMEs)-all of which are surface

expressions of deeper magnetic processes. These

magnetic phenomena have profound implications for

space weather, inﬂuencing the solar wind and impacting

Earth’s magnetosphere, satellites, and communication

systems. Researchers use a combination of numerical

simulations, helioseismic data, and observations from

solar missions to reﬁne dynamo models and better

predict solar behaviour. In essence, the solar dynamo

is not just a theoretical construct-it is a dynamic engine

driving the Sun’s magnetic heartbeat and shaping the

space environment throughout the solar system.

1.5 Wave-Particle Interactions

In the realm of solar and space plasma

physics, wave-particle interactions are fundamental to

understanding energy transfer and particle dynamics in

the Sun’s atmosphere. Plasma, being a magnetized

and ionized medium, supports a variety of wave

modes, notably Alfv

en waves and magnetosonic waves,

which arise from the coupling between magnetic

ﬁelds and charged particles. Alfv

en waves propagate

along magnetic ﬁeld lines and are driven by magnetic

tension, while magnetosonic waves combine pressure

and magnetic eﬀects, allowing them to travel across

ﬁeld lines. These waves play a critical role in

transporting energy and momentum from the lower solar

atmosphere into the corona. One of the most compelling

applications of this mechanism is in explaining the

coronal heating problem-why the solar corona reaches

temperatures exceeding one million Kelvin, far hotter

than the underlying photosphere. As these waves travel

upward, they undergo dissipation through resonant

absorption, phase mixing, and turbulent cascade,

eﬀectively heating the coronal plasma. Moreover,

during dynamic solar events such as ﬂares and coronal

mass ejections (CMEs), wave-particle interactions

become even more pronounced[7]. Particles can

gain energy through resonant interactions with plasma

waves, where their velocities match the wave phase

speed, leading to eﬃcient energy transfer. Additionally,

magnetic reconnection-a process where magnetic ﬁeld

Figure 2: Heliosphere

Image courtesy :Heliosphere.jpg (1600×1103)

lines break and reconnect-releases vast amounts of

stored magnetic energy, accelerating particles to high

energies. These mechanisms collectively contribute

to the generation of high-energy electrons and ions

observed during solar eruptions, which in turn produce

X-ray and radio emissions and inﬂuence space weather

conditions throughout the heliosphere.

1.6 Solar Wind and Heliosphere

The solar wind is a continuous outﬂow of charged

particles-primarily electrons and protons-from the Sun’s

upper atmosphere, driven by the immense thermal

and magnetic energy of the corona. This stream of

plasma expands outward, carving out a vast region

in space known as the heliosphere, which acts as a

protective bubble enveloping the entire solar system.

The heliosphere is shaped by the interplay between

the solar wind and the interstellar medium, with its

boundary-the heliopause-marking the point where solar

inﬂuence wanes and galactic forces dominate.

The behaviour of the solar wind is governed

by the principles of plasma physics, particularly

magnetohydrodynamics (MHD), which describes how

magnetic ﬁelds and ionized gases interact. As the solar

wind travels through space, it encounters planetary

magnetic ﬁelds, leading to complex interactions

that form magnetospheres-regions where a planet’s

magnetic ﬁeld deﬂects and channels the incoming

plasma. These interactions can compress, stretch, and

energize the magnetosphere, triggering phenomena

1.7 Observational and experimental tools in Solar Physics

Figure 3: Solar Dynamics Observatory

Image courtesy :NASA’s Solar Dynamics Observatory Celebrates Decade of Watching Sun

Sci.News

such as geomagnetic storms, auroras, and radiation belt

enhancements.

For Earth, such disturbances have tangible

consequences: they can disrupt satellite operations,

interfere with GPS and radio communications, and even

pose risks to astronauts and power grids. Understanding

the dynamics of the solar wind and its coupling

with planetary environments is thus critical for both

fundamental science and practical applications in space

weather forecasting. Missions like Voyager, Parker

Solar Probe, and Solar Orbiter continue to provide

invaluable data, helping researchers reﬁne models of

heliospheric structure and solar-terrestrial interactions.

1.7 Observational and experimental

tools in Solar Physics

In solar and space plasma research, observational

and experimental tools are indispensable for bridging

theory and reality. The Solar Dynamics Observatory

(SDO) and Parker Solar Probe are at the forefront of

high-resolution plasma imaging. SDO continuously

monitors the Sun in multiple wavelengths of extreme

ultraviolet light, capturing dynamic phenomena such

as ﬂares, coronal loops, and magnetic ﬁeld evolution.

Its data provide critical insight into the structure and

behaviour of the solar atmosphere[10]. Meanwhile,

Parker Solar Probe ventures closer to the Sun than any

spacecraft before, directly sampling the solar wind and

magnetic ﬁelds in the corona. This proximity allows

researchers to study plasma conditions in situ, oﬀering

unprecedented views of solar particle acceleration and

wave-particle interactions.

Complementing these imaging missions is

helioseismology, a technique akin to terrestrial

seismology but applied to the Sun. By analysing

oscillations-primarily pressure modes (p-modes)-on the

solar surface, scientists can infer the internal structure of

the Sun, including temperature gradients, ﬂow patterns,

and rotational dynamics. These oscillations are driven

by turbulent convection and provide a window into

otherwise inaccessible regions like the radiative zone

and the tachocline, a key layer for magnetic ﬁeld

generation.

Spectroscopy adds another layer of diagnostic

power. By examining the spectral lines emitted by solar

plasma, researchers can determine key parameters such

as temperature, density, and magnetic ﬁeld strength.

Spectral line broadening, Doppler shifts, and Zeeman

splitting are used to quantify these properties, enabling

detailed mapping of plasma conditions across diﬀerent

solar regions. These measurements are crucial for

understanding energy transport, magnetic reconnection,

and coronal heating mechanisms.

Together, these tools not only validate theoretical

models but also reﬁne our understanding of

plasma behaviour under extreme conditions-where

temperatures soar into the millions of Kelvin and

magnetic ﬁelds shape the dynamics of the solar system.

They form the backbone of modern heliophysics,

allowing researchers to decode the Sun’s inﬂuence

on space weather and planetary environments.

1.8

Relevance of Solar Plasma Physics

Studying solar plasma physics is not merely an

academic pursuit, it’s a gateway to solving some of the

most pressing scientiﬁc and technological challenges

of our time. At its core, solar plasma physics deepens

our understanding of stellar evolution, shedding light

on how stars are born, live, and die. The Sun serves

as a natural laboratory, allowing researchers to observe

plasma behaviour in real time, which in turn informs

models of more distant and exotic astrophysical objects

like pulsars, magnetars, and accretion disks around

black holes. This knowledge helps reﬁne theories of

energy transport, magnetic ﬁeld generation, and particle

1.9 Conclusion

acceleration across the universe.

On a more immediate scale, solar plasma research

is vital for space weather forecasting. The Sun’s activity-

ﬂares, coronal mass ejections, and solar wind variations-

can have dramatic eﬀects on Earth’s magnetosphere.

These disturbances can disrupt satellite operations, GPS

systems, aviation routes, and even electrical power grids.

By understanding the plasma processes that drive solar

eruptions, scientists can develop predictive models that

help mitigate risks to critical infrastructure and human

safety.

Finally, solar plasma physics oﬀers profound

insights into fusion energy, a potential cornerstone

of future clean energy solutions. The conditions

in the Sun’s core-extreme temperatures, magnetic

conﬁnement, and turbulent plasma dynamics-mirror

those in experimental fusion reactors like tokamaks

and stellarators. By studying how the Sun naturally

sustains nuclear fusion, researchers gain valuable clues

for optimizing conﬁnement, reducing instabilities, and

achieving energy-positive fusion on Earth. In essence,

solar plasma physics connects the cosmic with the

practical, advancing both our cosmic understanding and

our technological future.

1.9 Conclusion

Together, plasma physics and solar physics not

only deepen our understanding of stellar dynamics and

space weather but also inform terrestrial technologies,

from fusion energy research to satellite protection. This

interdisciplinary ﬁeld continues to evolve, bridging

theory and observation in the quest to unravel the

mysteries of our star and its inﬂuence on the solar

system.

References

[1] Francis, F, Chen., (2015). Introduction

to Plasma Physics and Controlled Fusion (3rd ed.).

Springer Cham. https://doi.org/10.1007/978-3-319-2

2309-4

[2]Jannet, G., et al., (2021). Measurement of

magnetic ﬁeld ﬂuctuations in the Parker Solar Probe

and Solar Orbiter missions. Journal of Geophysical

Research: Space Physics 126.2. https://doi.org/10.102

9/2020JA028543

[3] Velli, Marco, et al., (2020). Understanding the

origins of the heliosphere: integrating observations and

measurements from Parker Solar Probe, Solar Orbiter,

and other space-and ground-based observatories.

Astronomy & Astrophysics 642: A4.

[4] Roberts, B., (1991). Magnetohydrodynamic

waves in the Sun. Advances in Solar System

Magnetohydrodynamics 105

[5] Saha, Megh Nad. (1920). Elements in the

Sun.The London, Edinburgh, and Dublin Philosophical

Magazine and Journal of Science 40.240: 809-824

[6] Linker, J. A., Z. Miki

c, D. A. Biesecker, R. J.

Forsyth, S. E. Gibson, A. J. Lazarus, A. Lecinski,

P. Riley, A. Szabo, and B. J. Thompson., (1999),

“Magnetohydrodynamic modeling of the solar corona

during Whole Sun Month” J. Geophys. Res., 104(A5),

9809–9830, doi:10.1029/1998JA900159.

[7] Yashiro S, Gopalswamy N., (2008).

“Statistical relationship between solar ﬂares and

coronal mass ejections”. Proceedings of the

International Astronomical Union. 2008;4(S257):233-

243. doi:10.1017/S1743921309029342

[8] Charbonneau, Paul., (2014). Solar dynamo

theory. Annual Review of Astronomy and Astrophysics

52.1 : 251-290.

[9] Arnab Rai Choudhuri., ( 200). “An Elementary

Introduction to Solar Dynamo Theory”. AIP Conf.

Proc.; 919 (1): 49–73. https://doi.org/10.1063/1.2756

783

[10] Pesnell, W.D., Thompson, B.J., Chamberlin,

P.C., (2011). “The Solar Dynamics Observatory (SDO

In: Chamberlin, P., Pesnell, W.D., Thompson, B. (eds)

The Solar Dynamics Observatory. Springer, New York,

NY. https://doi.org/10.1007/978-1-4614-3673-7 2

1.9 Conclusion

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.

Black Hole Stories-21

The First LIGO Detection – A Binary Black

Hole

by Ajit Kembhavi

airis4D, Vol.3, No.9, 2025

www.airis4d.com

In this story we will describe the ﬁrst detection of

a gravitational wave source, a binary black hole, by the

LIGO detectors. The detection opened a completely

new window to the Universe. It is sobering to realise

that the binary could not have been detected by any

other means now available to us.

2.1 The First Detection

We have described in BHS-20 the Advance LIGO

detector (aLIGO), which were installed in the LIGO

observatories at Livingston, Louisiana and Hanford,

Washington State in the USA. By 2015, the engineering

runs, during which the systems were being validated,

had begun. During these runs, a signal, which could

be a burst of gravitational waves, was observed by the

detectors in the morning of September 14, 2015. The

signal was ﬁrst detected at the Livingston observatory,

and 7 milliseconds later in the detector at Hanford. The

delay in detection between the two detectors provides

a constraint on the direction in which the gravitational

wave could have approached the Earth and reached the

two detectors. This provides a broad region in the sky

where the source of the gravitational waves could be

located.

But is the signal really a burst of gravitational

waves? There are many other eﬀects which could lead

to spurious signals which mimic gravitational waves,

and these have to be carefully eliminated if the signal

is to be accepted as a real gravitational wave. The fact

that the signal was detected in both observatories within

a time span which was consistent with the distance

between them was very encouraging. Careful analysis

of the data led to the conclusion that the chance that

the signal is spurious is less than one in a million such

cases. The gravitational wave detection was therefore

taken to be genuine and was labelled as GW150914,

with the numbers indicating the date in the order year,

month, day.

2.2 The Nature of GW150914

The signal detected from GW150914 is shown in

Figure 1. The event as detected at Hanford is shown in

the upper panel of the ﬁgure, while in the lower panel

the event as detected by the two detectors are shown,

for comparison. The start of the signal was at 09:50:45

UTC (UTC is Coordinated Universal Time, which for

practical purposes coincides with the more familiar

GMT, which is Greenwich Mean Time). The time

measured from the start is shown on the horizontal axis

of the ﬁgure. The vertical axis shows the strain, which

is the change in length of the detector arm divided by its

steady length in the absence of the wave. As explained

in BHS-20, as the wave passes the detector, the arms

periodically becomes longer and shorter than their

steady length, so the strain goes from being positive

to negative to positive again and so on. Each such

2.3 A Model for the Black Hole Binary GW150914

Figure 1: The event GW150914. The upper panel

shows the signal detected at Hanford, while the lower

panel shows the detection at both the detectors, for

comparison. The axes are described in the text.

oscillation is known as a cycle.

The total time covered by the signal is about 0.2

seconds, during which the strain goes through about 8

cycles, with increasing height of the peak at each cycle.

The time interval between successive peaks in the signal

is shorter, which means that the frequency of the signal

increases for each cycle. Such a signal is known as

a chirp. The amplitude of the signal, i.e., the height

reached by the peaks, is maximum at a frequency of

about 150 Hertz. Beyond that, the amplitude decreases

over successive cycles, which is known as the ringdown.

The periodic nature of the signal shows that the

source was a binary system, with the two components

rapidly going round each other. The observed increasing

frequency of the gravitational waves meant that the rate

at which the two components go round each other, which

is the period of rotation, was increasing. It follows from

Kepler’s law the distance between the components must

be decreasing, i.e. , the components were spiralling

towards each other. The spiralling in is due to the

emission of gravitational waves, which leads to loss

of energy, making the total energy of the binary more

negative, which requires the size to shrink (see BHS-4,

BHS-6). The highest frequency the observed signal

reached was about 150 Hertz (cycles per second). It can

be shown from the theory of gravitational waves that

the frequency of the emitted gravitational wave is twice

the rotation rate. The two components of the binary

were therefore going round each other at about 75 times

a second at peak amplitude.

From the observed frequency of the gravitational

waves at some point of the orbit, and the rate at which

the frequency increases, it is possible to estimate the

total mass of the binary. This turns out to be greater

than 70 Solar masses. The sum of the Schwarzschild

radius of the two components must then be greater than

about 210 km, since one Solar mass corresponds to a

Schwarzschild radius of 3 km. The two components are

going round each other 75 times per second when the

maximum strain is reached. Assuming that the masses

are equal, the distance between them can be calculated

to be only 350 km. The components therefore have to

be very compact. If they were extended, they would

have smashed into each other long before the distance

between them reduced to 350 km, and in that case

the peak frequency of 150 Hz would not have been

observed.

Neutron stars are very compact, with a radius of

about 10 km. Could the observed binary have consisted

of two neutron stars? That is simply not possible, since a

neutron star can have a maximum mass about three Solar

masses, which is not enough to make up the estimated

mass of the binary. Another plausible conﬁguration

is a neutron star in orbit around a massive black hole.

But here again the binary would be disrupted before

the frequency of 150 Hertz is reached, because of the

tidal forces exerted by the black hole on the neutron

star. So a binary consisting of two massive black holes

is the best case for explaining the observations. We

will see below what happens to the binary after the two

components merge together.

2.3 A Model for the Black Hole

Binary GW150914

In the above, we have used simple Newtonian

dynamics of binary systems to argue that the source of

2.3 A Model for the Black Hole Binary GW150914

the observed gravitational waves was a binary system

of two black holes with a total mass of about 70 Solar

masses. But the gravitational forces involved are very

strong and detailed modelling of the system requires

the use of the general theory of relativity. The analysis

involves mathematical calculations as well as the use

of numerical methods. Such analysis is used to predict

the shape of the signal which would be observed from

black hole binaries with a wide range of properties. By

comparing the predicted shape with the observed signal,

the parameters which lead to the best agreement can be

determined.

The result is that the binary system, which emitted

the gravitational wave signal, was at a distance of about

1.34 billion light years from us. The mass of the two

black holes was 36 Solar masses and 29 Solar masses

respectively, so that the combined mass before the

merger was 65 Solar masses (the estimate above from

Newtonian dynamics was

70 Solar masses). As

described above, the binary contracts in size as the two

black holes rapidly spiral in. The eventual merger leads

to the formation of a rapidly spinning black hole with a

mass of 62 Solar masses. The mass of the ﬁnal black

hole is therefore less than the combined mass of the two

black holes in the binary by three Solar masses.

What happened to the diﬀerence of about 3 Solar

masses? According the special theory of relativity,

mass and energy are equivalent, so the missing mass

must have been emitted from the system as energy.

Since the components of the binary are black holes, no

electromagnetic energy can be emitted, and the only

form of emission possible is gravitational waves. It is

this emission which leads to the contraction of the binary.

It can be shown, from the theory of gravitational wave

emission from binary sources, that the total amount of

gravitational wave energy emitted by the system is equal

to the energy corresponding to three Solar masses. This

is exactly equal to the mass missing after the formation

of the single black hole. The gravitational energy was

emitted in a fraction of a second, and at its peak the rate

of emission was equivalent to converting 200 times the

mass of the Sun to energy in a second.

After the peak frequency is reached, the two black

holes merge together and the waveform enters what is

known as the ringdown phase through which the merged

object settles down to a black hole which has only two

properties. These are the mass, which for GW150914 is

62 Solar masses, and spin, which is equivalent to about

100 rotations per second. Such an object is known as a

Kerr black hole (see BHS-9). The irregularities present

in the object soon after the merger are all radiated away

as gravitational waves, leaving behind a pristine black

hole described by just the mass and spin.

The observed ringdown phase agrees perfectly with

the form predicted from a theoretical calculation by C.

V. Vishveshwara in 1970. The calculated waveform

emitted by the binary is shown in the upper panel of

Figure 2, along with a sketch of the merging binary. The

shape of the single black hole soon after the merger, and

after the ringdown, are also shown. These have been

obtained using numerical calculations. The decreasing

separation of the two components, and the increasing

velocity are shown in the lower panel of the ﬁgure. The

waveform shows the increasing frequency as the black

holes approach each other, the peak amplitude reached,

and the ringdown after the merger. At their closest

distance before merger, the black holes are moving

relative to each other with a very high velocity of 0.6

times the velocity of light.

The binary black hole emits absolutely no

electromagnetic waves, so it could not have been

observed by any means other than the gravitational

waves detected by aLIGO. The discovery established

the existence of black holes, binary black holes,

gravitational waves and the correctness of Einstein’s

theory in describing the system. It therefore ranks as

one of the great discoveries in the history of physics

and astronomy, which yet again demonstrates how the

building of novel, large telescopes and detectors always

leads to startling new discoveries. The discovery is

of such great importance that the 2017 Noble prize in

physics was awarded jointly to the American physicists

Reiner Weiss, Barry C. Barish and Kip S. Thorne for

their decisive contribution to the LIGO detector and the

observation of gravitational waves’’.

It is interesting that the detection was made a

century after the formulation of the general theory

of relativity by Albert Einstein in 1915, and the

2.3 A Model for the Black Hole Binary GW150914

Figure 2: The horizontal axis shows the time elapsed

after the start of the detection of gravitational waves.

The observations are limited to just 0.2 seconds, at the

end of which the merger is completed. In the upper

panel a sketch of the binary and the shapes soon after

the merger and after the ringdown are shown. In the

middle panel, the vertical axis corresponds to the strain,

which is the fractional change in length of the arms of

the detector due to passage of the gravitational wave.

The theoretically and numerically calculated waveform,

consistent with the observations, is shown in the panel.

In the lower panel, the vertical axis on the left indicates

the velocity with which the black holes are moving in

the orbit, as a fraction of the velocity of light. The

vertical axis on the right shows the separation of the

black holes in units of the Schwarzschild radius. The

highest velocity reached at peak amplitude is about 0.6

times the velocity of light. The separation reduces to

zero at the merger.

Figure courtesy B. P. Abbot et. al., Phys. Rev. Letts 116, 06112, 2016.

announcement was made a century after Einstein ﬁrst

predicted the existence of gravitational waves in 1916!

In the following stories we will describe other black

hole binaries detected as gravitational wave sources,

and possible formation mechanisms for these objects.

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: Through Missions

by Aromal P

airis4D, Vol.3, No.9, 2025

www.airis4d.com

3.1 Satellites in 2015s

The second half of the 2010s marked the

emergence of a new space power: China. As with

many ﬁelds of science and astronomy, China invested

in X-ray astronomy as well. They began their journey

into the X-ray sky with the launch of small satellites,

including the X-ray Pulsar Navigation (XPNAV-1) and

several CubeSats. Additionally, China launched the

Insight-HXMT during this decade. This period also

witnessed the launch of some of the most sophisticated

instruments in the ﬁeld, including NASA’s NICER

and the SRG project developed by Russia and the

Max Planck Institute (MPE). We will discuss these

observatories in more detail in this article.

3.2 NICER

The Neutron star Interior Composition

Explore(NICER) is an externally attached Payload

to the ExPRESS Logistics Carrier-2 (ELC-2) of

International Space Station(ISS). NICER was launched

on June, 2017 in SpaceX’s falcon 9 rocket in the

Dragon cargo spacecraft as a supply to the ISS. It was

later ﬁxed in the mentioned position using robotic

arms and astronauts onboard of the ISS. NICER

follow orbiter parameters as same as that of ISS.

The mission’s primary scientiﬁc objectives include

measuring neutron star radii with unprecedented

precision (better than 10% uncertainty), constraining

the equation of state of matter at supranuclear densities,

and exploring the dynamics of matter under extreme

gravitational ﬁelds. NICER was originally designed

Figure 1: NICER’s 56 X-ray Concentrator optics

integrated onto the Concentrator Plate in NICER’s

dedicated coalign- ment facility Credits:[1].

for an 18-month baseline mission, but has far exceeded

expectations, now operating in its eighth year. NICER

has encountered light leak issues while operating and it

is resolved by another special mission.

NICER’s primary scientiﬁc instrument is the X-ray

Timing Instrument (XTI), which consists of an aligned

collection of 56 X-ray concentrator optics (XRC) and

silicon drift detector (SDD) pairs. NICER did not have

imaging capabilities. SDDs are fast-response silicon

devices that enable high throughput and rapid temporal

response while maintaining excellent spectral resolution.

Each X-ray concentrator consists of 24 nested parabolic

gold-coated thin foil mirrors designed to concentrate

(rather than focus) X-ray photons from cosmic sources

onto the detectors. NICER XTI operates in an energy

range of 0.2-12 keV, has a timing resolution of 300

nanoseconds and a ﬁeld of View of 30 arcminutes for

each concentrator [1]

NICER’s most celebrated achievement came in

December 2019 with the ﬁrst accurate measurement

of both the mass and radius of a neutron star,

PSR J0030+0451. This groundbreaking observation

revealed a pulsar with a mass of 1.44 ± 0.15 solar

masses and a radius of 13.02 km (with uncertainties of

3.3 Insight-HXMT

Figure 2: Orientations for all telescopes in insight-

HXMT. Creadits:[3]

+1.24/-1.06 km)[

]. Remarkably, NICER also produced

the ﬁrst-ever map of a pulsar’s surface, revealing hot

spots with temperatures exceeding one million degrees

arranged in unexpected conﬁgurations on the pulsar’s

southern hemisphere.

3.3 Insight-HXMT

The Insight-HXMT (Hard X-ray Modulation

Telescope) is China’s ﬁrst X-ray astronomy satellite.

It was launched on June, 2017, from Jiuquan Satellite

Launch Center aboard a Long March 4B rocket. The

satellite was placed in a low earth orbit of 550 km

radius with an inclination of

∼ 43

○

which gives an

orbital period of 96 minutes. Initially proposed lifespan

of 4 years, the satellite is still operational. This satellite

has three scientiﬁc payloads that work in an energy

band between 1.0-250 keV [3].

The Low Energy X-ray telescope (LE): LE contains

three detector boxes with a sun buﬀer each. One LE

detector box contains two modules and each module

contains 16 Swept Charge Device (SCD) chips, and

every four SCD detectors share one collimator. SCD’s

are a special kind of CCD which can oﬀer pile-up free

observations. The total detection area of each module is

64 cm

. It works in an energy range of 1.0-15 keV. LE

oﬀers a timing resolution of 0.98 ms and have Multiple

conﬁgurations FOVs of 1.6°×6°, 4°×6°, 50°-60°×2°-6°

for diﬀerent detector boxes. The LE telescope excels

in studying the spectral and temporal properties of soft

X-ray sources, with extremely low pileup rates

The Medium Energy X-ray telescope (ME):

consisted of three individual Si-PIN detector boxes.

It works in an energy range of 5-30 keV. The eﬀective

area of the instrument is

∼ 952 cm

. It oﬀers a spectral

resolution of

∼ 3

keV at 17.8 keV and a Time Resolution

of 255 µs

High Energy X-ray Telescope (HE): HE consists

of 18 NaI(Tl)/CsI(Na) phoswich detectors with a total

geometrical area of about

5100 cm

. It has an energy

resolution of

∼ 15%

at 60 keV and a timing resolution

< 10µs

. HE has a combined FOV od

5.7

○

5.7

○

The CsI(Na) component serves dual purposes as active

shielding for NaI(Tl) and as an omnidirectional gamma-

ray monitor for GRB detection in the 0.2-3 MeV range.

Insight-HXMT has been instrumental in studying

newly discovered black hole X-ray binaries, providing

unprecedented insights into their fundamental

properties. The telescope measured precise

spin parameters for several black hole systems[

The mission has provided detailed phase-resolved

spectroscopy of numerous X-ray pulsars, revealing

complex relationships between cyclotron line energies,

pulse phases, and luminosity. Insight-HXMT conducted

extensive monitoring of SGR J1935+2154 during its

active episodes, detecting over 300 bursts during

a 20-day observation in October 2022. The

mission characterized burst spectral properties, phase

distributions, and statistical behaviors, ﬁnding evidence

for self-organizing criticality in magnetar burst

processes[5].

3.4 Spektr-RG

Spektr-RG (Spectrum-R

ontgen-Gamma), also

known as SRG, is a joint Russian-German high-energy

astrophysics space observatory launched on July, 2019.

The mission represents the most signiﬁcant Russian

space science project in the post-Soviet era and serves

as the second observatory in the Spektr series of space

telescopes. The primary scientiﬁc goal of Spektr-RG is

to create an unprecedented X-ray map of the Universe,

identifying all large galaxy clusters in the observable

cosmos. SRG is located at the L2 Legrangian point,

which gives continuous unprecedented observation of

the sky away from the sun throughout the year. SRG

consists of mainly two scientiﬁc payloads that covers a

total energy band of 0.2-30 keV.

eROSITA (Extended Roentgen Survey with an

Imaging Telescope Array): eROSITA is the primary

REFERENCES

Figure 3: The full view of the X-ray sky monitored by

the eRosita mission. Credits:Public Domain

German-built instrument developed by the Max Planck

Institute for Extraterrestrial Physics (MPE). The

telescope features seven identical mirror modules

arranged in a hexagonal conﬁguration. Each module

contains 54 nested mirror shells in Wolter-I geometry.

It works in the energy range of 0.2-8 keV and oﬀers a

FOV of 1 degree. The eROSITA telescope structure

measures approximately 1.9 m in diameter by 3.2 m

in height. Each camera assembly includes a pn-CCD

detector with 384 × 384 pixels, providing excellent

spectral resolution. It also has an eﬀective area of

2400 cm

at 1 keV[6].

ART-XC (Astronomical Roentgen Telescope -

X-ray Concentrator): ART-XC is the Russian-built

instrument developed by the Space Research Institute

(IKI) and the All-Russian Scientiﬁc Research Institute

for Experimental Physics (VNIIEF). It consists of seven

identical mirror modules with nested mirror shells.

Each module contains 28 nested conical mirror shells

in Wolter-I geometry. The ART-XC detectors are

Double-sided Silicon Strip Detectors (DSSDs) made

of cadmium telluride (CdTe). It operates in the energy

range of 4-30 keV with an eﬀective area of

385 cm

8.1 keV[7]

One of Spektr-RG’s most remarkable achievements

is its discovery and cataloging of galaxy clusters on

an unprecedented scale. The ﬁrst eROSITA All-Sky

Survey (eRASS1) resulted in the identiﬁcation of 12,247

optically conﬁrmed galaxy clusters and groups, with

68% (8,361) being entirely new discoveries previously

unknown to science[

]. Spektr-RG has dramatically

expanded our catalog of supermassive black holes

throughout the universe. This mission is expected to

detect approximately 3 million supermassive black holes

in active galactic nuclei (AGN) during its operational

lifetime. Recent achievements include the discovery

of 11 new active galactic nuclei at relatively nearby

distances (redshifts 0.028-0.258), and the identiﬁcation

of 13 tidal disruption events (TDEs)[8]

References

[1]

Gendreau et al. SPIE Astronomical Telescopes +

Instrumentation 2016

[2]

Miller et al. The Astrophysical Journal Letters 2019

[3]

Zhang et al. Science China Physics, Mechanics &

Astronomy 2020

[4]

Peng et al. The Astrophysical Journal Letters 2024

[5] Chatterjee et al. The Astrophysical Journal 2024

[6] Predehl et al. Astronomy & Astrophysics 2021

[7] Pavlinsky et al. Astronomy & Astrophysics 2021

[8]

Sazonov et al. Monthly Notices of the Royal

Astronomical Society 2021

[9] Bulbul et al. Astronomy & Astrophysics 2024

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.

Isochrones in Astronomy

by Sindhu G

airis4D, Vol.3, No.9, 2025

www.airis4d.com

4.1 Introduction

In stellar astrophysics, one of the most powerful

tools to study the properties, structure, and evolution of

stars is the isochrone. Derived from the Greek words

for “equal” (iso) and “time” (chronos), isochrones are

curves on the Hertzsprung–Russell (HR) diagram or

the color–magnitude diagram (CMD) that represent

the theoretical positions of stars of diﬀerent masses

but the same age and chemical composition. These

curves allow astronomers to compare observed stellar

populations with theoretical models of stellar evolution.

Isochrones are essential for a wide range of

astronomical studies, including determining the ages

of star clusters, tracing the history of star formation

in galaxies, and understanding stellar structure and

nucleosynthesis. This article discusses the theoretical

basis of isochrones, their construction, applications in

astronomy, and the challenges associated with their use.

4.2 Theoretical Basis of Isochrones

Isochrones are derived from stellar evolution

models, which track the changes in a star’s luminosity,

eﬀective temperature, and internal structure over time

as it burns nuclear fuel. The position of a star on the

HR diagram at a given time depends primarily on three

parameters:

Initial Mass – Determines the rate of stellar

evolution. High-mass stars evolve rapidly and

end their lives as supernovae, while low-mass

stars evolve more slowly and may live for billions

of years.

Figure 1: Theoretical isochrones from the stellar

models by Leo Girardi and collaborators (Padova) for

near-solar metallicity and a range of ages. Image Credit:

Ivan Ramirez.

Chemical Composition (Metallicity) – Heavier

element abundance aﬀects stellar opacity, energy

transport, and nuclear burning rates.

Age – Stars of the same age but diﬀerent masses

occupy diﬀerent positions on the HR diagram.

By computing stellar evolutionary tracks for stars

of diﬀerent masses, astrophysicists can construct a

set of points corresponding to stars of a given age

and metallicity. Connecting these points results in an

isochrone.

Figure 1 shows theoretical isochrones of diﬀerent ages (5

Myr, 20 Myr, 100 Myr, 1 Gyr, and 10 Gyr). The x-axis

4.3 Construction of Isochrones

represents the stellar surface temperature (decreasing

to the right), while the y-axis shows luminosity in

units of solar luminosity (

L/L

⊙

). Each colored curve

corresponds to stars of the same age but diﬀerent masses.

The diagonal band represents the main sequence, where

stars burn hydrogen in their cores. The ”turnoﬀ point”

(where the curve bends away from the main sequence)

shifts downward with increasing age and provides a

direct estimate of the stellar population’s age. Stars

moving upward and rightward are evolving into red

giants after exhausting core hydrogen. Isochrone ﬁtting

to observed cluster stars allows astronomers to estimate

the cluster’s age.

4.3 Construction of Isochrones

The construction of isochrones requires

sophisticated numerical modeling of stellar structure

and evolution. The key steps include:

Stellar Evolutionary Tracks: Compute

evolutionary tracks for a grid of stellar masses

over time using stellar structure equations and

input physics such as nuclear reaction rates,

opacities, and equations of state.

Isochrone Interpolation: For a given age,

extract points from each stellar track where the

star has reached that age. Interpolate between

tracks of diﬀerent masses to create a continuous

curve.

Transformation to Observables: Convert

theoretical parameters (luminosity, eﬀective

temperature) into observational quantities

(magnitudes and colors) using bolometric

corrections and stellar atmosphere models.

Extinction and Distance Corrections: Apply

corrections for interstellar dust extinction and

distance modulus to compare with observational

data.

Many public databases, such as the Padova

isochrones and MESA Isochrones and Stellar Tracks

(MIST), provide pre-computed isochrones for a wide

range of ages and metallicities, which are widely used

in stellar population studies.

4.4 Applications of Isochrones

Isochrones are indispensable in many ﬁelds of

astronomy:

4.4.1 Age Dating of Star Clusters

Star clusters are nearly coeval stellar populations.

By ﬁtting isochrones to the observed CMD of a cluster,

astronomers can estimate its age. The position of the

main-sequence turnoﬀ point, where stars begin to leave

the main sequence, is particularly sensitive to age.

4.4.2 Determining Distances

If the metallicity and age are known, ﬁtting an

isochrone to the observed CMD provides the distance

modulus of the cluster or stellar population. This

technique is often used in combination with parallax

measurements.

4.4.3 Chemical Evolution Studies

Isochrones for diﬀerent metallicities allow

astronomers to trace the chemical enrichment history

of galaxies. Comparing isochrones with observed

stellar populations helps determine the distribution

of metallicities and ages.

4.4.4 Exoplanet Host Stars

The properties of exoplanetary systems depend

strongly on their host stars. Isochrone ﬁtting helps

determine stellar radii, masses, and ages, which in turn

constrain planetary properties.

4.4.5 Galactic Archaeology

Large surveys such as Gaia provide photometric

and astrometric data for millions of stars. Isochrone

ﬁtting across these datasets helps reconstruct the star

formation history and assembly of the Milky Way.

4.5 Challenges and Limitations

Despite their usefulness, isochrones face several

challenges:

4.6 Future Prospects

Uncertainties in Stellar Physics: Processes such

as convection, rotation, mass loss, and magnetic

ﬁelds are diﬃcult to model accurately, leading to

uncertainties in isochrone predictions.

Binary Stars: Many stars exist in binary or

multiple systems. Interactions between stars

can alter their evolution, making single-star

isochrones inadequate.

Extinction and Reddening: Dust obscuration

complicates the comparison of isochrones with

observed CMDs, especially in star-forming

regions.

Degeneracies: Similar CMD features can

result from diﬀerent combinations of age,

metallicity, and distance, making unique

solutions challenging.

4.6 Future Prospects

The future of isochrone applications in astronomy

is bright, with improvements in both observational data

and theoretical models:

Gaia Mission: The unprecedented precision

of Gaia parallaxes and photometry allows more

accurate CMDs, enabling reﬁned isochrone ﬁtting

for millions of stars.

Advanced Stellar Models: Incorporation of

rotation, asteroseismic constraints, and 3D stellar

atmosphere models will lead to more realistic

isochrones.

Machine Learning Techniques: Automated

ﬁtting of isochrones to large datasets is becoming

possible through artiﬁcial intelligence and

Bayesian inference methods.

4.7 Conclusion

Isochrones are fundamental tools in stellar

astrophysics, bridging theoretical stellar evolution

models with observational data. They provide insights

into the ages, distances, and chemical compositions

of stars and stellar systems, and are essential in ﬁelds

ranging from star cluster studies to galactic archaeology

and exoplanet science. While uncertainties remain,

continued advances in both theory and observations

promise to reﬁne isochrones and expand their role in

understanding the universe.

In summary, isochrones serve as a cosmic clock,

allowing astronomers to piece together the life stories

of stars and galaxies across cosmic time.

References:

OVERVIEW OF STELLAR POPULATION

SYNTHESIS

MESA ISOCHRONES AND STELLAR

TRACKS (MIST) 0:METHODS FOR

THE CONSTRUCTION OF STELLAR

ISOCHRONES

Stellar evolution and modelling stars

Comparative Study of Diﬀerent Stellar Tracks

and Isochrones

Stellar models and isochrones from low-mass to

massive stars including pre-main sequence phase

with accretion

About the Author

Sindhu G is a research scholar in the

Department of Physics at St. Thomas College,

Kozhencherry. She is doing research in Astronomy

& Astrophysics, with her work primarily focusing

on the classiﬁcation of variable stars using diﬀerent

machine learning algorithms. She is also involved

in period prediction for various types of variable

stars—especially eclipsing binaries—and in the study

of optical counterparts of X-ray binaries.

Part III

Biosciences

The Symphony of Signal: A Deep Dive into

Neurotransmission

by Geetha Paul

airis4D, Vol.3, No.9, 2025

www.airis4d.com

1.1 Introduction

Neurotransmission, the fundamental language of

the nervous system, is the process by which neurons

transmit signals to each other or to other cells. Neurons

have a specialised anatomy consisting of dendrites

(signal receivers), a cell body, and a long axon

(signal transmitter). Signals are initiated by an action

potential, an electrical impulse travelling down the

axon. A process by which neurons communicate

through chemical and electrical signals, analogous

to a symphony with various players contributing

to a harmonious outcome. Neurotransmission

involves multiple components such as neurotransmitters,

receptors, ion channels, and signal propagation, all

working in concert to enable brain functions like thought,

emotion, sensation, and movement.

Neurons (also called neurones or nerve cells) are

the fundamental units of the brain and nervous system,

the cells responsible for receiving sensory input from

the external world, for sending motor commands to our

muscles, and for transforming and relaying the electrical

signals at every step in between. More than that, their

interactions deﬁne who we are as people. Having said

that, our roughly 100 billion neurons do interact closely

with other cell types, broadly classiﬁed as glia (these

may actually outnumber neurons, although it’s not really

known).

There are billions of neurons and thousands of

varieties of neurons; they can be classiﬁed into three

basic groups based on function. These are motor

Figure 1: Structure of a typical neuron. Nervous system

cells are called neurons. They have three distinct parts,

including a cell body, axon, and dendrites. These parts

help them to send and receive chemical and electrical

signals.

Image courtesy: https://www.healthline.com/health/neurons

neurons, sensory neurons, and interneurons.

This network operates through a process called

neurotransmission, an elegant game of chemistry

and electricity that allows billions of neurons to

converse, creating everything from the spark of a

thought to the surge of fear, from a deliberate movement

to a subconscious heartbeat. Two of the most

potent messengers in this system are dopamine and

adrenaline (epinephrine). Dopamine, the currency

of reward, motivation, and ﬁne motor control, and

adrenaline, the architect of the primal ’ﬁght-or-ﬂight’

response, exemplify how chemical molecules can

produce profound physical and psychological eﬀects.

While their outcomes diﬀer dramatically, their journey

from one cell to the next follows a precise and universal

sequence of events, a testament to the elegant design of

1.2 Steps involved in the process of Neurotransmission

Figure 2: Communication between twoneurons

happens in the synaptic cleft (the small gap between

the synapses of neurons). Here, electrical signals that

have travelled along the axon are brieﬂy converted into

chemical ones through the release of neurotransmitters,

causing a speciﬁc response in the receiving neuron.

Image courtesy: https://qbi.uq.edu.au/brain/brain-functions/what-are-neurotransmitters

Figure 3: An action potential, or spike, causes

neurotransmitters to be released across the synaptic

cleft, causing an electrical signal in the postsynaptic

neuron.

Image courtesy: Thomas Splettstoesser

biological signalling.

1.2 Steps involved in the process of

Neurotransmission

1.2.1 Synthesis and Storage - Crafting the

Message

The process begins not with a signal, but with

preparation. Within the cytoplasm of the presynaptic

neuron (the sending cell), the raw materials for

communication are gathered. Using enzymes and

precursors derived from our diet, like the amino

acid tyrosine, the neuron meticulously manufactures

its neurotransmitter cargo. For a dopamine neuron,

this means converting tyrosine through a series of

steps into dopamine itself. For a cell producing

adrenaline, the process is more complex, involving

the conversion of dopamine ﬁrst into noradrenaline and

then into adrenaline within the adrenal medulla. Once

synthesised, these powerful chemical messages are not

left to drift freely. They are immediately packaged and

sealed into tiny, spherical membranes called synaptic

vesicles. These vesicles act as protective storage units,

safeguarding the neurotransmitters from degradation

and concentrating them in the axon terminal, poised for

immediate release the moment the command arrives.

1.2.2 Release - Launching the Signal into the

Void

The command for release is an electrical impulse

known as an action potential. This wave of

depolarisation rockets down the axon of the neuron

like a fuse until it reaches the terminal button. Its

arrival triggers the opening of voltage-gated calcium

(

[207A]

) channels. The inﬂux of calcium ions acts

as the critical trigger, causing the synaptic vesicles to

shudder, migrate toward the cell membrane, and fuse

with it. In an explosive process called exocytosis, the

vesicles empty their contents directly into the synaptic

cleft, the minuscule, ﬂuid-ﬁlled gap that separates the

sending neuron from the receiving one. In an instant,

the carefully crafted neurotransmitters, be it dopamine

1.3 Monoamine neurotransmitters

or adrenaline, are launched into the synaptic void, their

fate now dependent on random diﬀusion.

1.2.3 Receptor Binding - The Lock and Key

The journey across the synaptic cleft is brief but

random. On the other side, the postsynaptic membrane

(of the receiving cell) is studded with specialised

protein structures called receptors. Each receptor

has a unique geometric shape, making it speciﬁc to

a particular neurotransmitter, a classic lock and key

mechanism. A dopamine molecule will only ﬁt into a

dopamine receptor, and adrenaline into an adrenergic

receptor. The binding of the neurotransmitter to its

receptor is a pivotal moment of recognition. It causes a

conformational change in the receptor protein. For

ionotropic receptors, this change opens a channel,

allowing ions to ﬂood in and alter the cell’s electrical

charge. For G-protein-coupled receptors (GPCRs),

common for dopamine and adrenaline, the binding

activates an internal G-protein, initiating a powerful

secondary messenger cascade within the cell that can

amplify the signal and create diverse, widespread

eﬀects.

1.2.4 Postynaptic Eﬀect - The Cellular

Response

The binding event translates the chemical signal

back to the postsynaptic cell. The receptor type and

the cell itself determine the nature of this response. If

the eﬀect is excitatory, it depolarises the cell, making

it more likely to generate its own action potential and

propagate the signal. This is often adrenaline’s eﬀect on

the heart. If the eﬀect is inhibitory, it hyperpolarises

the cell, suppressing its activity and quieting the

neural circuit. Certain dopamine pathways provide this

inhibitory eﬀect. Beyond immediate electrical changes,

the secondary messenger systems activated by GPCRs

can unleash long-lasting biochemical alterations, such

as activating enzymes, altering metabolism, or even

triggering changes in gene expression, thereby forging

long-term memories and adaptations.

1.2.5 Inactivation - Ending the Conversation

Precision in communication requires not just

power, but also control. To prevent the signal from

persisting indeﬁnitely and causing erratic ﬁring or

damage, the neurotransmitter must be rapidly removed

from the synaptic cleft. This critical process of

inactivation occurs through three primary mechanisms.

The most common is reuptake, where specialised

transporter proteins on the presynaptic neuron act

like molecular vacuum cleaners, actively pumping the

neurotransmitter (e.g., dopamine) back into the terminal

for recycling and reuse. Alternatively, enzymes in the

cleft, such as monoamine oxidase (MAO) and catechol-

O-methyltransferase (COMT), can break down the

molecules into inactive metabolites. Finally, simple

diﬀusion can see some molecules drift away from the

synapse entirely, diluting their concentration and ending

their eﬀect. This swift clearance resets the synapse,

making it ready to receive the next message with clarity

and precision.

1.3 Monoamine neurotransmitters

Monoamine neurotransmitters play a crucial part

in regulating a wide range of functions in the nervous

system, and their eﬀects are especially prominent in

the brain. They help manage consciousness, thinking,

attention, and mood. Disorders of the nervous system

often arise from imbalances in these chemicals, and

many medications target these pathways to treat various

conditions.

1.3.1 Serotonin

Serotonin is primarily an inhibitory

neurotransmitter involved in stabilising mood,

sleep cycles, appetite, sexual function, anxiety, and

pain perception. Imbalances in serotonin levels have

been linked to conditions such as seasonal aﬀective

disorder, anxiety disorders, depression, ﬁbromyalgia,

and chronic pain. Medications like SSRIs and SNRIs,

which adjust serotonin activity, are widely prescribed

for these issues.

1.4 Catechol-O-methyltransferase (COMT)

1.3.2 Histamine

Histamine is important for maintaining

wakefulness, regulating appetite, and motivating

feeding behavior. It is implicated in responses linked to

asthma, bronchospasm, mucosal swelling, and multiple

sclerosis. This neurotransmitter helps modulate

immune responses as well as brain activity.

1.3.3 Dopamine

Dopamine is central to the body’s reward

system, driving pleasure, arousal, and learning.

It inﬂuences focus, memory, sleep, motivation,

mood, and concentration. Disorders tied to

dopamine irregularities include Parkinson’s disease,

schizophrenia, bipolar disorder, restless legs syndrome,

and ADHD. Many addictive substances, such as cocaine

and amphetamines, impact dopamine pathways directly.

1.3.4 Epinephrine

Also known as adrenaline, epinephrine is a key

driver of the body’s “ﬁght-or-ﬂight” reaction to stress

or fear. It boosts heart rate, breathing, blood pressure,

blood sugar, and muscle readiness, sharpening attention

and enabling rapid responses to threats. Excess

epinephrine is associated with high blood pressure,

diabetes, and heart disease. As a drug, it is used for

conditions such as severe allergic reactions, asthma

attacks, cardiac arrest, and serious infections.

1.3.5 Norepinephrine

Norepinephrine, or noradrenaline, increases blood

pressure and heart rate, and is well-known for

boosting alertness, arousal, decision-making, and focus.

Medications targeting norepinephrine can improve

symptoms of ADHD and depression by enhancing

concentration and mood.

These monoamine neurotransmitters not only

underlie basic emotional and cognitive processes, but

also represent central targets for many psychiatric and

neurological therapies

1.4 Catechol-O-methyltransferase

(COMT)

It is a crucial enzyme that plays a vital role in

the metabolism and inactivation of catecholamines, a

class of key neurotransmitters and hormones in the

human body. Its primary function is to terminate

the biological activity of these signalling molecules,

ensuring that neural and hormonal messages are precise,

brief, and controlled. Parkinson’s Disease patients are

often treated with L-DOPA (Levodopa), a dopamine

precursor. COMT inhibitors (e.g., Entacapone,

Tolcapone) are prescribed alongside L-DOPA to block

its breakdown in the periphery, allowing more L-

DOPA to reach the brain and become dopamine. This

enhances the therapeutic eﬀect and smooths out motor

ﬂuctuations.

1.5 Acetylcholine

Acetylcholine was the ﬁrst neurotransmitter to be

discovered, identiﬁed as a small but vital molecule in

the nervous system. It was initially recognised for its

key function in the peripheral nervous system, where

it is secreted by motor neurons and by neurons of

the autonomic nervous system to control numerous

body processes. Within the central nervous system,

acetylcholine is crucial for supporting cognitive abilities,

and its deﬁciency, particularly from damage to

cholinergic neurons, has been linked to Alzheimer’s

disease. This excitatory neurotransmitter serves

essential roles in both the central nervous system

(comprising the brain and spinal cord) and the peripheral

nervous system (all nerves branching outside the

CNS). Most neurons in the autonomic nervous system

release acetylcholine, thereby inﬂuencing heart rate, the

regulation of blood pressure, and intestinal movement.

Additionally, acetylcholine is involved in muscle

contraction, memory formation, motivation, sexual

desire, sleep cycles, and learning. Variations in its levels

are associated with health issues such as Alzheimer’s

disease, seizures, and muscle spasms.

1.6 Conclusion

Neurotransmission is the exquisitely precise

process that forms the fundamental language of the

nervous system, converting electrical impulses into

chemical signals to orchestrate all brain and bodily

functions. It begins with the synthesis and release

of neurotransmitters like dopamine into the synaptic

cleft, where they bind to speciﬁc receptors on the

target cell, delivering commands that alter its activity

through excitation or inhibition. Crucially, the signal is

rapidly terminated via reuptake or enzymatic breakdown

by molecules like COMT, ensuring communication

remains brief and controlled. This elegant cycle from

release to inactivation is the indispensable mechanism

underlying every thought, memory, emotion, and action,

highlighting the profound link between molecular

biology and conscious experience.

References

https://www.ncbi.nlm.nih.gov/books/NBK1111

https://qbi.uq.edu.au/brain/brain-functions/wha

t-are-neurotransmitters

https://qbi.uq.edu.au/brain-basics/brain/brain-p

hysiology/action-potentials-and-synapses

https://www.healthline.com/health/neurons

About the Author

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.