Cover page

Image Name: Mating Orthetrum sabina

During mating, the slender skimmer dragonﬂy, Orthetrum sabina, forms a copulatory wheel, or tandem position,

where the female curls her abdomen under her body to receive sperm from the male. The male holds her by

the back of her neck until eggs are laid to ensure that the oﬀspring are his. Dragonﬂies, being one of the oldest

species known to exist from the Jurassic period, are very selective about their environment and are widely used

as bioindicators.

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.2, No.10, 2024

www.airis4d.com

This edition starts with the article ”Asynchronous

Programming with Python” by Arun Aniyan, which ex-

plains how asynchronous programming improves eﬃ-

ciency in handling multiple tasks, especially I/O-bound

operations, by allowing tasks to pause and resume with-

out blocking other processes. Python’s asyncio mod-

ule is key for writing async code, using coroutines,

tasks, and the event loop to manage concurrent op-

erations. ThreadPoolExecutor is used for parallelism

in CPU-bound tasks. Overall, async programming en-

hances performance and scalability, making it valuable

for building high-performance applications in Python.

Linn Abraham’s article ”What the Stars Taught

Us” explores the profound lessons from studying stars.

Stars’ brightness and colour, plotted on the H-R dia-

gram, revealed critical insights into their nature. The

article explains how stars generate energy through nu-

clear fusion and exist in a plasma state, held together

by gravity. It also touches on the elements essential for

life, like carbon and oxygen, produced in stars. Ulti-

mately, the stars teach us that we are made from ”star

dust” and that life on Earth is powered by starlight.

In the article ”How is the Spin of a Black Hole

Estimated?” Ajit Kembhavi” - Black Hole Stories-12

- explains that astrophysical black holes can be char-

acterised by their mass and spin. A key method for

estimating black hole spin involves analysing the in-

nermost stable circular orbit (ISCO) around the black

hole, which changes based on the black hole’s spin.

Accretion disks, common around black holes, emit X-

rays, and studying the broadening of iron emission

lines (caused by relativistic eﬀects) can provide clues

about the spin. For spinning black holes, the ISCO

moves closer to the event horizon, leading to a more

pronounced broadening of the iron line, as observed

in active galaxies like MCG-6-30-15. These obser-

vations help estimate black hole spin, mainly through

X-ray emissions from the accretion disk.

The article ”The Hertzsprung-Russell Diagram

and Colour-Magnitude Diagram of the Pleiades” by

Sindhu G discusses two key tools in astrophysics—the

Hertzsprung-Russell (H-R) diagram and the Colour-

Magnitude Diagram (CMD). These diagrams help as-

tronomers study star clusters like the Pleiades by visual-

ising stellar evolution and star properties such as bright-

ness, temperature, and composition. The Pleiades clus-

ter, located 444 light-years from Earth and about 100

million years old, has a prominent main sequence of

stars that aids in determining its age and evolutionary

stage. Comparing the Pleiades with other clusters, such

as the Hyades, highlights the evolutionary diﬀerences

between star populations. Both the H-R diagram and

CMD are essential for understanding the life cycles of

stars and the history of stellar formation.

The article ”Brachythemis contaminata as Bio-

Indicators of Water Pollution” by Geetha Paul explores

the role of the dragonﬂy species Brachythemis con-

taminata (Ditch jewel) as an eﬀective bio-indicator for

assessing water pollution, particularly in agricultural

landscapes. This species is highly tolerant of polluted

environments, thriving in water bodies with high levels

of pollutants such as nitrates and phosphates from agri-

cultural runoﬀ. Unlike many other sensitive Odonates,

B. contaminata can survive in degraded water with low

oxygen levels, making it a valuable species for ecolog-

ical monitoring.

Studies show that the presence and population

density of B. contaminata are strongly correlated with

poor water quality, marked by high biochemical oxygen

demand (BOD) and low dissolved oxygen (DO) lev-

els. This adaptability allows researchers to assess the

health of aquatic ecosystems by tracking this species’

behaviour and distribution, making it a cost-eﬀective

tool for monitoring environmental health and water

quality.

The article ”Multi-Omics: A New Era in Biomarker

Discovery” by Jinsu Ann Mathew explores how multi-

omics integrates data from genomics, proteomics, tran-

scriptomics, epigenomics, and metabolomics to under-

stand complex biological systems better. This com-

bined approach helps identify more accurate biomark-

ers for disease diagnosis and personalised treatments.

Each omics layer oﬀers unique insights, providing a

comprehensive view of the molecular mechanisms be-

hind health and disease. Multi-omics transforms re-

search and precision medicine, leading to more eﬀec-

tive and personalised healthcare solutions.

The article ”Promise of Synthetic Aperture Radars

for Remote Sensing” by Balamuralidhar P highlights

the potential of Synthetic Aperture Radar (SAR) in

revolutionising remote sensing. SAR uses radio waves

to capture high-resolution images in all weather con-

ditions, making it ideal for 24/7 monitoring. It can

penetrate surfaces like vegetation and ice, providing

valuable data for various applications, including dis-

aster management, precision agriculture, and environ-

mental conservation.

SAR’s ability to detect acceptable changes in in-

frastructure, track urban growth, and monitor illegal

activities like deforestation and ﬁshing oﬀers signiﬁ-

cant advantages. However, challenges like high power

consumption and complex data processing persist. Ad-

vances in technology, including smaller, more cost-

eﬀective SAR satellites and improved data process-

ing techniques, are helping overcome these limitations,

making SAR a critical tool in monitoring and managing

global environmental and infrastructure changes.

iii

Contents

Editorial ii

I Artiﬁcial Intelligence and Machine Learning 1

1 Asynchronous Programming with Python 2

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

1.2 Python Async . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Getting Started with asyncio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 CPU Bound Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

II Astronomy and Astrophysics 7

1 Black Hole Stories-12

How is the Spin of a Black Hole Estimated? 8

1.1 Black Holes With and Without Spin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.2 Accretion Discs Around Black Holes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3 The Iron Emission line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4 The Shape of the Iron Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.5 MCG 6-30-15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 What the Stars Taught Us? 14

2.1 Stardust and Starlight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Brightness and Colour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Physics of Stars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 The Hertzsprung-Russell Diagram and Colour-Magnitude Diagram of the Pleiades 17

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 The Hertzsprung-Russell (H-R) Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 The Colour-Magnitude Diagram (CMD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.4 The Pleiades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.5 The H-R Diagram of the Pleiades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.6 The Colour-Magnitude Diagram of the Pleiades . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.7 Comparing the H-R Diagram and CMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.8 Comparative Analysis with Other Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

CONTENTS

III Biosciences 21

1 Brachythemis contaminata as Bio-Indicators of Water Pollution: A Study in Agricul-

tural Landscapes 22

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.2 Characteristics of Brachythemis contaminata (Ditch jewel) . . . . . . . . . . . . . . . . . . . . . 22

1.3 Tolerance to Polluted Water Bodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.4 Correlation with Agricultural Runoﬀ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.5 Water Quality Parameters Linked to Brachythemis contaminata Presence . . . . . . . . . . . . . 24

1.6 Comparisons with Other Odonates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2 Multi-Omics: A New Era in Biomarker Discovery 26

2.1 Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2 Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3 Transcriptomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.4 Epigenomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.5 Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.6 Summary of Omic Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

IV Remote Sensing 30

1 Promise of Synthetic Aperture Radars for Remote Sensing 31

1.1 Disaster Management and Climate Change Monitoring . . . . . . . . . . . . . . . . . . . . . . . 31

Part I

Artiﬁcial Intelligence and Machine Learning

Asynchronous Programming with Python

by Arun Aniyan

DeepAlert Ltd., UK

airis4D, Vol.2, No.10, 2024

www.airis4d.com

1.1 Introduction

When cooking a complex meal, you wouldn’t just

stand and watch each pot boil one at a time, would

you? Instead, you would start one dish, then while it’s

cooking, you would work on another. This is the basic

idea behind asynchronous programming.

In the world of computers, many tasks involve

waiting. For example, when your program needs to

fetch data from the internet or read a large ﬁle, it spends

a lot of time just waiting for these operations to ﬁnish.

In real-world applications that run on large scales, for

example, a service that responds to millions of users

make use of asynchronous codes running in parallel.

Traditional (or synchronous) programming is like

cooking one dish at a time. The program does one

thing, waits for it to ﬁnish, then moves on to the next

thing. This can be ineﬃcient, especially when dealing

with tasks that involve a lot of waiting.

Asynchronous programming (Async), on the other

hand, is like our eﬃcient cooking method. It allows

a program to start a task that might take a while (like

boiling water), and then work on other tasks while wait-

ing. When the ﬁrst task is done (the water is boiling),

the program can come back to it and continue (add the

pasta).

This approach has several beneﬁts:

Eﬃciency: Your program can do other useful

work instead of just waiting around.

Responsiveness: In applications with a user in-

terface, async programming can help keep the

app responsive even while it’s doing time con-

suming tasks.

Scalability: Async programs can often han-

dle more concurrent operations with fewer re-

sources compared to traditional multi threaded

programs.

Asynchronous programming is a powerful paradigm

that allows developers to write concurrent code without

the complexity of traditional multi-threading. Asyn-

chronous programming is a way to write concurrent

code that can handle multiple tasks simultaneously

without blocking the execution of the main program.

This is particularly useful for I/O-bound operations,

such as network requests or ﬁle operations, where the

program spends a lot of time waiting for external re-

sources.

In traditional synchronous programming, oper-

ations are executed sequentially, and each operation

must be completed before the next one begins. This

can lead to ineﬃcient use of resources, especially when

dealing with time-consuming I/O operations.

Asynchronous programming allows the program

to continue executing other tasks while waiting for I/O

operations to complete, thereby improving overall per-

formance and responsiveness.

1.2 Python Async

In Python, we use the asyncio module to write

asynchronous code. It provides tools that allow us to

write code that can switch between tasks eﬃciently,

making the most of our computer’s resources.

There are a few key concepts of async coding

1.2 Python Async

with Python. They are considered the building blocks

of aync coding.

1.2.1 Coroutines

Imagine a coroutine as a special function that can

take a break in the middle of its work. Unlike regular

functions that run from start to ﬁnish without interrup-

tion, coroutines can pause their execution, allow other

code to run, and then resume where they left oﬀ.

In Python, we create coroutines using the async

def syntax. Here’s a simple example:

async def my_coroutine():

print("Starting work")

# Imagine some time-consuming task here

print("Finishing work")

The function as a whole executes asynchronously

but will do some other tasks in between.

1.2.2 Event Loop

The event loop is like a smart task manager for

your async program. It keeps track of all the corou-

tines and decides which one should run next. When

a coroutine takes a break (for example, to wait for

some data), the event loop switches to another corou-

tine that’s ready to do some work.

In Python’s asyncio, you usually don’t need to

create the event loop yourself. The asyncio.run()

function takes care of it for you. The following is an

example code snippet.

import asyncio

asyncio.run(my_coroutine())

1.2.3 Awaitables

“Awaitable” is a term for anything that you can use

with the await keyword. The most common awaitables

are:

Coroutines

Tasks

Futures

When you await something, you are telling your

coroutine to pause until that operation is complete, like

the snippet below.

async def main():

result = await some_async_function()

print(result)

1.2.4 Tasks

A task is a wrapper around a coroutine that sched-

ules it to run on the event loop. Tasks are useful when

you want to run multiple coroutines concurrently. You

can create a task using asyncio.create task().

async def main():

task1 = asyncio.create_task(some_coro

⌋

utine())↪

task2 = asyncio.create_task(another_c

⌋

oroutine())↪

await task1

await task2

The above code basically shows an example of

two async tasks being created and the completion of

task1 and task2 are done asynchronously.

1.2.5 Futures

A future is an object that represents a result that

hasn’t been calculated yet. It’s a low-level tool that you

might not use directly very often, but it’s good to know

about. Tasks are built on top of futures.

Think of a future as an empty box that will even-

tually contain the result of an operation. When the

operation is done, the box gets ﬁlled.

1.2.6 Piecing all together

The above concepts can be summarized in the

following manner to write an async code.

Write coroutines to deﬁne chunks of work that

can be paused and resumed.

Create tasks from these coroutines to schedule

them for execution.

The event loop manages these tasks, running

them concurrently.

Inside your coroutines, you use await to pause ex-

ecution when you’re waiting for something (like

data from a network).

While one coroutine is paused, the event loop

can run other coroutines.

This system allows your program to juggle multiple

operations eﬃciently, especially when those operations

1.3 Getting Started with asyncio

involve a lot of waiting (like network requests or ﬁle

I/O).

In essence,

Use async def to deﬁne coroutines.

Use await when you want to pause and wait for

something.

Use asyncio.run() to run your main async

function.

1.3 Getting Started with asyncio

As we have the basic concepts layed out, let’s dive

into writing some asynchronous code using Python’s

asyncio module. We’ll start with a hello world exam-

ple of an asyncio code.

import asyncio

async def hello_world():

print("Hello")

await asyncio.sleep(1) # Pause for 1

second↪

print("World")

asyncio.run(hello_world())

Let’s look at this code step by step.

1. We import the asyncio module, which provides

all the tools we need for async programming.

2. We deﬁne an async function (also called a corou-

tine) using async def. This tells Python that

this function can be paused and resumed.

3. Inside the function, we print ”Hello”, then use

await asyncio.sleep(1) to pause for one

second. The await keyword is used whenever

we want to pause our coroutine and let other code

run.

4. After the pause, we print ”World”.

5. Finally, we use asyncio.run(hello world())

to run our coroutine.

This function sets up the event loop and runs our

coroutine until it’s complete.

1.3.1 Running multiple coroutines

One of the main advantages of async program-

ming is the ability to run multiple operations

concurrently. Shown below is an example of

this.

import asyncio

async def count_to_three():

for i in range(1, 4):

print(f"Counting: {i}")

await asyncio.sleep(1)

async def greet():

for greeting in ["Hello", "Bonjour",

"Hola"]:↪

print(f"Greeting: {greeting}")

await asyncio.sleep(1.5)

async def main():

count_task =

asyncio.create_task(count_to_three())↪

greet_task = asyncio.create_task(greet())

await count_task

await greet_task

asyncio.run(main())

The explanation of the code can be done as fol-

lows.

We deﬁne two coroutines: count to three()

and greet(). Each of these functions does

some work (printing) and then pauses.

In our main() coroutine, we use asyncio.create task()

to schedule both of these coroutines to run

concurrently.

We then await both tasks, which means our

main() function will wait until both tasks

are complete before ﬁnishing.

You’ll see the counting and greeting messages inter-

leaved when you run this code. This is because while

one coroutine is paused (during the sleep()), the other

one gets a chance to run. This is a simple example to

show running operation concurrently.

1.3.2 Running multiple coroutines and

waiting

Let’s say that you would like to run multiple tasks,

and wait until each of them completes. This is where

the asyncio.gather() method can be used. The

following is an example code.

1.4 CPU Bound Tasks

import asyncio

async def fetch_data(name):

print(f"Start fetching data for {name}")

await asyncio.sleep(2) # Simulate a

2-second data fetch↪

print(f"Finished fetching data for

{name}")↪

return f"Data for {name}"

async def main():

results = await asyncio.gather(

fetch_data("User 1"),

fetch_data("User 2"),

fetch_data("User 3")

)

print(results)

asyncio.run(main())

In the above example, we deﬁne a fetch data

coroutine that simulates fetching data for a user. In the

main() function, we use asyncio.gather() to run

three instances of fetch data concurrently.

asyncio.gather() returns a list of the results from

all the coroutines, which we then print. When we run

this code, we will see that all three ”fetch” operations

start almost simultaneously, and they all ﬁnish after

about 2 seconds (not 6 seconds, which it would take if

they ran one after the other).

1.4 CPU Bound Tasks

In many cases, the async operations may be I/O

bound, meaning the CPU is not doing any heavy lift-

ing. For complex calculations that are CPU-bound, the

previously explained methods are not ideal because

they may lock the CPU to a single operation. For

CPU-bound tasks, we can use concurrent.futures.

ThreadPoolExecutor in combination with asyncio.

This allows us to run CPU-intensive operations without

blocking the event loop.

Let us try to understand what a Threadpoolexe-

cuter is ﬁrst. Imagine you have a big box of tasks that

need to be done, and a team of workers ready to do

them. A ThreadPoolExecutor is like a manager that

hands out these tasks to the workers. Each worker (or

thread) can work on a task independently.

asyncio is single-threaded, meaning it can only

do one thing at a time on a single CPU core. This is ﬁne

for I/O-bound tasks because the program spends most

of its time waiting anyway. But for CPU-bound tasks,

we need to use multiple CPU cores to achieve true

parallelism. That’s where ThreadPoolExecutor comes

in. Let us look at an example as shown below.

import asyncio

import concurrent.futures

import time

def cpu_bound_task(n):

"""A CPU-intensive task that calculates

the sum of squares."""↪

return sum(i * i for i in range(n))

async def main():

loop = asyncio.get_running_loop()

# Create a ThreadPoolExecutor

with concurrent.futures.ThreadPoolExecuto

⌋

r() as pool:↪

# Run two CPU-bound tasks

concurrently↪

results = await asyncio.gather(

loop.run_in_executor(pool,

cpu_bound_task, 10**7),↪

loop.run_in_executor(pool,

cpu_bound_task, 10**7)↪

)

print(f"Results: {results}")

# Measure the time it takes to run

start = time.time()

asyncio.run(main())

end = time.time()

print(f"Time taken: {end - start:.2f}

seconds")↪

The breakdown of the above code is as follows.

We deﬁne a CPU-bound task cpu bound task

that calculates the sum of squares up to a given

number. This is a synchronous function.

In the main() function we have an asyncio.

get running loop() object which handles syn-

chronous methods in async contexts.

Next within a

concurrent.futures.ThreadPoolExecutor() con-

text we run the CPU bound async operations.

We use loop.run in executor() to run our CPU-

1.5 Conclusion

bound task in the thread pool. This function returns

a coroutine, which we can await.

We use asyncio.gather() to run two of these tasks

concurrently.

If we run this script, you’ll notice it completes faster

than if you ran the two tasks one after the other. This is

because the two tasks are running in parallel on diﬀer-

ent CPU cores. Without using ThreadPoolExecutor,

our CPU-bound tasks would block the event loop, pre-

venting other asynchronous tasks from running. By us-

ing ThreadPoolExecutor, we can run CPU-intensive

tasks alongside our regular asyncio code without block-

ing.

When using ThreadPoolExecutor, there are a

few things that we need to bear in mind.

1. Thread safety: When using threads, you need

to be careful about shared resources. Make sure

your code is thread-safe.

2. Number of threads: By default, ThreadPoolEx-

ecutor creates a number of threads equal to the

number of CPUs on your machine. You can

specify a diﬀerent number if needed.

3. Overhead: Creating and managing threads has

some overhead. For very short tasks, this over-

head might outweigh the beneﬁts of parallelism.

4. Use sparingly: While ThreadPoolExecutor is

powerful, it’s not a replacement for asyncio.

Use it only for CPU-bound tasks that would oth-

erwise block your event loop.

By combining asyncio with ThreadPoolExecutor, you

can write eﬃcient, concurrent code that handles both

I/O-bound and CPU-bound tasks eﬀectively. This makes

it possible to build complex, high performance applica-

tions that make the most of your computer’s resources.

1.5 Conclusion

Asynchronous programming in Python with asyncio

oﬀers a powerful way to write eﬃcient, concurrent

code. By leveraging coroutines, the event loop, and

other asyncio primitives, developers can create re-

sponsive applications that handle I/O-bound operations

with ease. While there is a learning curve associated

with async programming, the beneﬁts in terms of per-

formance and scalability make it a valuable skill for

any Python developer. As you continue to explore

asyncio, you’ll discover more advanced features and

patterns that can help you write even more sophisticated

asynchronous applications. Remember to always con-

sider the speciﬁc needs of your project when deciding

between synchronous and asynchronous approaches,

and don’t hesitate to refer to the oﬃcial Python doc-

umentation for more detailed information on asyncio

and its capabilities.

Reference

Python Asyncio

Async IO in Python: A Complete Walkthrough

Asyncio in Python: A Guide

About the Author

Dr.Arun Aniyan is leading the R&D for Arti-

ﬁcial intelligence at DeepAlert Ltd,UK. He comes from

an academic background and has experience in design-

ing machine learning products for diﬀerent domains.

His major interest is knowledge representation and com-

puter vision.

Part II

Astronomy and Astrophysics

Black Hole Stories-12

How is the Spin of a Black Hole Estimated?

by Ajit Kembhavi

airis4D, Vol.2, No.10, 2024

www.airis4d.com

We have learnt through our earlier stories that

astrophysical black holes can have only two properties,

mass and spin. Theoretically, a black hole can have an

electric charge, but that is not possible because matter

in the Universe is electrically neutral in the large scale,

and it such matter which forms a black hole. In our very

ﬁrst story, we have considered how the mass of a black

hole in a binary system, or at the centre of a galaxy, can

be measured. In this story we will recount how the spin

of a black hole can be estimated. Many of the concepts

used have been described in earlier stories, which the

reader should refer to for clariﬁcations.

1.1 Black Holes With and Without

Spin

A back hole with zero spin is described by the

Schwarzschild metric which is spherically symmet-

ric, while a spinning black hole is described by the

Kerr metric which has only axial symmetry. In the

Schwarzschild case, the angular momentum of a par-

ticle with mass, or a photon, in orbit around the black

hole is conserved, and the trajectories can be analysed

in terms of an eﬀective potential. For a particle circular

orbits are possible when for a given angular momen-

tum, the negative energy of the particle is low enough

for it to be at the bottom of the eﬀective potential. Now,

if the particle happens to lose some of its angular mo-

mentum, then the eﬀective potential changes in such a

way that the minimum in it moves inwards, i.e. closer

to the black hole. Because of the stronger gravitational

ﬁeld, the energy of a particle is now more negative.

Another change is that the minimum in the potential

becomes shallower, and the value of the eﬀective po-

tential at the minimum moves closer to the value at the

maximum (see Figure 2 in BHS7). If the angular mo-

mentum continues to decrease, the minimum eventu-

ally coincides with the maximum at a value of the angu-

lar momentum L = 2

√

3GM

. The circular orbit at this

value of L is known as the innermost stable circular or-

bit (ISCO) which has the radius r

ISCO

6GM

= 3RS.

Orbits with radius less than rISCO are not possible be-

cause as L reduces below 2

√

3GM

, there is no potential

well. r

ISCO

therefore is the smallest possible orbital

radius for a given mass M of the black hole. A parti-

cle which has suﬃciently small angular momentum to

reach r

ISCO

would continue to spiral in and eventually

enter the event horizon. When the particle is at rest

at a large distance, the total energy of the particle is

simply its rest mass energy mc

. Because a particle in

a bound orbit must have come from a great distance,

energy has been removed from it since the total energy

in the bound orbit is negative, while it is mc

at a great

distance. It can be shown that when a particle has fallen

to the Schwarzschild innermost stable circular orbit at

ISCO

, the energy extracted from the particle is 5.7

percent of its rest mass.

The Kerr case is far more complicated than the

Schwarzschild case and a large variety of trajectories

is possible. The simplest trajectories are those corre-

sponding to paths of particles moving in a plane per-

1.2 Accretion Discs Around Black Holes

pendicular the spin axis of the black hole. Analysis

of these trajectories shows that the ISCO for the Kerr

case occurs when the black hole has (1) the maximum

possible spin allowed by the constraints of the Kerr

geometry and (2) the sense of the particle rotation is

the same as the spin of the black. This tightest possible

circular orbit has the radius r

ISCO,Kerr

This radius being less than the Schwarzschild radius

is not a matter of concern, since in the Kerr case the

size of the event horizon is less than

. A particle

which reaches r

ISCO,Kerr

would have lost 0.42 of its

rest mass energy, so that would be the eﬃciency of the

energy extraction.

In this story we mainly concerned with how close

a particle can get to the black hole and yet remain in

a circular orbit, as that lets us detect a non-zero spin

if it is present. We will see below how that becomes

possible through the agency of an accretion disc.

1.2 Accretion Discs Around Black

Holes

We have seen in BHS-1 that black holes can be

detected when there is matter falling on them. For

stellar mass black holes that happens when a black hole

forms a binary system with a companion star. Given the

right conditions, matter from the companion star ﬂows

onto the black hole and gets heated in the process,

which leads to the emission of X-rays and radiation at

other wavelengths which can be observed. In the case

of a supermassive black hole at the centre of a galaxy,

the matter mainly comes from stars which are disrupted

due to tidal forces on them due the gravitational ﬁeld

of the black hole. In both cases an accretion disc acts

as an intermediary.

How is an accretion disc formed? In the two cases

mentioned above, matter falling on the black hole has

angular momentum and therefore it cannot fall radially

onto the black hole, it spirals in a nearly circular orbit

towards the hole. This is like the motion of a single

particle in the eﬀective potential of the black hole,

which we have considered at length in previous stories.

In the process, a layer of gas which is closer to the

hole moves faster than the next layer away from the

centre. The inner layer then loses energy and angular

momentum due to the viscous drag and move inwards.

In this manner the incoming stream of matter forms a

disc like structure which is known as an accretion disc.

If the accretion were happening onto a compact object

like a neutron star, it could extend right to the surface

of the object. The situation is diﬀerent for a black hole,

because of the existence of r

ISCO

. As the gas moves

closer to the black hole than r

ISCO

, it can no longer

move in stable orbits and plunges into the black hole.

So the inner radius of the accretion disc can at most

reach this radial coordinate.

Accretion discs are formed in a variety of astro-

physical circumstances, here we are concerned with

accretion discs around black holes in binary systems

and around active galactic nuclei which are found at the

centres of active galaxies. The dissipation of energy in

such an accretion disc causes it to heat up, with the tem-

perature reaching high values ˜105 K, so they emit in

the optical and ultraviolet regions of the spectrum. But

we also observe strong X-ray emission from X-ray bi-

nary systems and quasars, which require an additional

mechanism for their production. That is provided by

a corona of hot gas, at a temperature of ˜108 K which

is located some distance from the accretion disc, as

shown in Figure 1.

Some of the thermal photons which are emitted by

the disc reach the corona, where they undergo inverse

Compton scattering, in which energy is transferred to

the photons by the highly energetic electrons in the hot

corona. The photon energy is then in the X-ray range,

and the photons have a power-law spectrum for which

the intensity I (ν)ν −α, where ν is the frequency and α

is a constant, typically in the range 0-1.5. A plot of the

logarithm of I(ν) against the logarithm of the frequency

is a straight line of negative slope, as shown in Figure

2. Some of these photons escape, while others are

scattered back to the accretion disc, where they again

undergo multiple scattering in the colder material in the

disc, with some of the photons escaping from the disc.

These photons are said to be reﬂected from the disc.

The process leads to the production of an emission line

of iron, which is relevant to our discussion of the spin

1.3 The Iron Emission line

Figure 1: A sketch showing an accretion disc and a

corona and the various components of the radiation

which are explained in the text. The temperatures are

indicated in kilo electron volts (keV), with 1 KeV being

equivalent to 11.6 million K. The inner disc is indicated

to be at the innermost stable circular orbit.

measurement of a black hole.

1.3 The Iron Emission line

The X-ray spectrum of many active galaxies, which

harbour a supermassive black hole and produce an elec-

tromagnetic spectrum like a quasar, contains an emis-

sion feature at ∼ 6.4 keV, which is identiﬁed to be line

emission from iron. This feature provides very im-

portant insight into the way the observed spectrum is

produced in the region around the centre. The line is

found to be broad, which means that it is spread over

a signiﬁcant wavelength interval. There is a good case

to say that the line originates in a region which is very

compact and the eﬀects of strong gravity have therefore

to be taken into account in explaining the proﬁle of the

emission line.

X-rays coming to the accretion disc from the corona

have energy in the keV range so they can photoionise

an electron from the inner shells of atoms of heavy

elements. The atom is then in an excited state, and

can lose energy when the vacant state is ﬁlled by an

electron from one of the higher levels, leading to the

emission of a photon. The process is known as ﬂu-

orescence. In an alternative process, known as the

Auger eﬀect, the ﬁlling of the vacancy is accompanied

by the ejection of an electron from a higher shell, and

the atom loses energy in a radiation-less manner. The

ﬂuorescent yield of a shell is a measure of the fraction

Figure 2: A power-law spectrum is indicated as a

dashed line. The other line indicates a spectrum pro-

duced by reﬂection from an accretion disc, as in Figure

1. The position of the broad iron line is indicated. Th

frequency and intensity axis are on a logarithmic scale.

of cases an atom loses energy through the emission

of a photon, following ionization from that shell. The

intensity of an emission line produced through ﬂuores-

cence depends on the product of the ﬂuorescent yield

for the shell and the abundance of the element. This

product is the highest for iron, and therefore it is the

iron ﬂuorescence which is the most important in the

X-ray spectra of active galactic nuclei. The energy of

the iron Kα varies from 6.4 keV for neutral iron to 6.9

keV for Fe XXVI, which is iron with all but one elec-

tron removed, with the diﬀerence in energy being due

to the screening eﬀect of the electrons when they are

present.

Iron emission and absorption features were estab-

lished as being common in the spectra of AGN through

observation by the X-ray satellite GINGA in the early

1990s. In these observations, an emission line was

found with energy close to 6.4 keV, which is consistent

with the feature being the Kα line due to ﬂuorescence

from cold (neutral) iron by reﬂection from an accretion

disc in the manner described above. The spectrum that

a distant observer sees is a combination of the orig-

inal power-law spectrum and the reﬂected spectrum,

with the iron line. The spectrum at low energies is

also altered by the absorption it undergoes in traveling

1.5 MCG 6-30-15

through partially ionized matter. The iron line pro-

duced is very narrow at emission, but can be broadened

due to various eﬀects as described below.

1.4 The Shape of the Iron Line

The narrow iron emission line generated in the

disc is broadened by three eﬀects: (1) Doppler shift

due to the rotation of the disc, (2) transverse Doppler

shift and (3) gravitational redshift. The shape of the

low energy part of the broadened line depends on how

close to the centre the emission can occur, and that lets

us estimate the spin of the black hole.

(1) Disc Rotation: The disc is in rotation around

the black hole, with an annulus at a radius r rotating at

a speed given by the Keplerian value v = (

)

. The

radiation emitted from parts of the annulus moving

away from the observer is found to have lesser pho-

ton energy than at emission, due to the Doppler eﬀect.

The observed wavelength of the radiation is therefore

greater than the emitted wavelength, and the reddened

radiation is said to be redshifted. Similarly, radiation

emitted from parts of the annulus moving towards the

observer has higher photon energy, or decreased wave-

length, and is said to be blue shifted. The annuli of the

disk which are closer to the centre rotate at a greater

velocity, so that the Doppler eﬀect on radiation from

the inner region is greater. As shown in the upper

panel of Figure 3, the rotation results in a two horned

structure for the line proﬁle. The two lines in the upper

panel correspond to emission from two annuli, which

are marked as two dashed lines in the disc shown on the

left of ﬁgure. Such a line proﬁle is obtained for emis-

sion from a rotating disk, when the rotation velocity

is non-relativistic, i.e., small compared to the speed of

light. But the X-ray emission being considered occurs

from a compact region close to the centre, where the

gravitational ﬁeld is strong, and the velocities are high

and special relativistic eﬀects become important. This

results in increase in the intensity of the radiation from

the blue shifted peak, due to the relativistic beaming

eﬀect, which makes the blue peak taller than the red-

shifted peak as shown in the second panel from the top

in Figure 3.

(2) Transverse Doppler Eﬀect: The high velocities

lead to a transverse Doppler eﬀect predicted by the

special theory of relativity, which occurs when there

is relative transverse motion between an emitter and a

receiver. This eﬀect is included in the shape of the line

shown in the second panel of Figure 3.

(3) The shape of the line also depends on the gen-

eral relativistic gravitational redshift, which causes the

photons to lose energy as they move from a region with

strong gravity to the distant observer. The magnitude

of the eﬀect depends on how close to the centre a pho-

ton is emitted. The inﬂuence of the redshift eﬀect on

the line proﬁle emitted by the disc is shown in the third

panel of the ﬁgure.

The above eﬀects acting together broaden and

smear the emission line, as shown in the bottom panel

for the Schwarzschild case. How much the line is

broadened depends on how close the inner part of the

disk gets to the black hole, and the inclination of the

disk relative to a distant observer. For a Schwarzschild

black hole, which has mass but no spin, the minimum

possible radius that the inner part of the disk can reach

is r

SCO =

6GM

. For a spinning black hole, the min-

imum distance reached isr

ISCO,Kerr

, when the

black hole has the maximum spin permitted by general

relativity (see BHS10), and the orbit is in the same

sense as the spin of the black hole. Therefore, for a

spinning black hole the inner part of the disk can be lo-

cated closer to the black hole than in the Schwarzschild

case. The gravity is that much stronger and the ro-

tation speed of the disc is higher near r

ISCO,Kerr

which makes the line greatly broadened. The observed

breadth of the line should make it possible to estimate

the spin of the black hole. The diﬀerence between the

two cases can clearly be seen in Figure 4. The y-axis

of the ﬁgure is indicative of the intensity of radiation,

while the x-axis shows the energy of the photon ob-

served. Photon energies lower and higher than the iron

emission line value of ˜6.4 keV are due to redshift and

blue shift of the radiation due to the eﬀects discussed

above.

1.5 MCG 6-30-15

A very well studied active galaxy for the shape

of the iron line is the Seyfert galaxy MCG-6-30-15,

in which the iron emission is particularly strong be-

cause the iron abundance is about 2 times the Solar

value. This galaxy was observed by the X-ray satellite

ASCA in a long exposure of about four days, with a

detector with very good energy resolution, in 1995 by

Tanaka and his collaborators. The proﬁle of the line

was found to be made up of two parts: A narrow com-

ponent around 6.4 keV and a broad part extending to

lower energies. Detailed modelling showed that asym-

metric proﬁle most probably arose from the innermost

parts of an accretion disk around a Schwarzschild black

hole, the region of emission extending from

6GM

10GM c

from the centre. The shape of the line has

been later studied in greater detail by several X-ray

missions. Results from the Suzaku and XMM-Newton

missions are shown in Figure 5. Here the y-axis is in-

dicative of the intensity observed at an energy indicated

along the x-axis. The x-ais shows the redshifted and

blue shifted energy of photons which begin as 6.4 keV

iron line photons with a narrow spread. For photons of

such low energy to be observed, the inner part of the

accretion disc needs to reach within <

2.2GM

with 90

percent conﬁdence. For the r

ISCO

to reach this value

the angular momentum parameter a > 0.917. These

observations therefore clearly indicate the presence of a

spinning black hole, rather than a Schwarzschild black

hole, though we do not have a precise value for the

spin.

Next Story: In the next story we will continue

with the discussion on black hole spin measurements.

Figure 3: On the left of the ﬁgure is shown an accretion

disc, with two annuli marked in white. On the right are

four panels which show the eﬀect of various processes

on the shape of a narrow emission line of iron. Details

are provided in Section 4. The ﬁgure is from Fabian

etal, Publications of the Astronomical Society of the

paciﬁc (PASJ) 1995, p1145.

Figure 4: Iron line proﬁles generated by an accretions

disc around a Schwarzschild black hole (the proﬁle

with the peaks) and an accretion disc around a spinning

Kerr black hole (the other proﬁle). The x-axis shows

the energy of photons after processing, which redshifts

or blue shifts the energy. It is seen that the Kerr proﬁle

extends to lower energy than the Schwarzschild proﬁle,

because smaller radial coordinates values are reached

in the Kerr case. The y-axis is proportional to the in-

tensity of the radiation. Details are provided in Section

4. The ﬁgure is from Fabian et.al., Publications of

the Astronomical Society of the paciﬁc (PASJ) 1995,

p1145.

1.5 MCG 6-30-15

Figure 5: The ﬁgure shows the proﬁle of the 6.4 keV

iron line as observed by the Suzaku and XMM-Newton

X-ray mission. The low energy reached indicates the

presence of a spinning black hole, as explained in the

text. The ﬁgure is from G. Miniutti et. al., Publications

of the Astronomical Society of Japan 2007, p S315.

About the Author

Professor Ajit Kembhavi is an emeritus

Professor at Inter University Centre for Astronomy

and Astrophysics and is also the Principal Investiga-

tor 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 ac-

tivities from the late 80s to promote astronomy re-

search in Indian universities. The Speak with an

Astronomer monthly interactive program to answer

questions based on his article will allow young enthu-

siasts to gain profound knowledge about the topic.

What the Stars Taught Us?

by Linn Abraham

airis4D, Vol.2, No.10, 2024

www.airis4d.com

2.1 Stardust and Starlight

When people ﬁrst gazed into the depths of the

night sky and saw numerous pinpricks of light, it might

not have occurred to them that these “stars” had a cru-

cial role in the existence of the very same life forms that

stood to stare back at them. The ancients were inter-

ested in ﬁnding out what all matter was fun- damentally

made up of. The elements that they came up with was

ﬁre, water, earth, air, etc. However we know today that

the elements that actually make up all matter consti-

tutes the periodic table starting from hydrogen. But we

also know that these elements also appear in diﬀerent

abundances on Earth. The Earth’s crust is mostly com-

posed of minerals that are silicon based. We also know

that all life on Earth is ultimately carbon-based. The

other dominant ele- ments are oxygen, hydrogen, nitro-

gen, phosporous, sulfur. So the ﬁrst question we must

ask is the following. How did these elements come to

be ? Why do they exist in the proportions in which they

do today? A second observation is about the nature of

life. All processes in nature tend to replace order with

disorder. Life is the only process that violates this. The

law of nature is to turn cathedrals into rocks but it is

us humans that attempt to do the opposite. How is this

possible? The answer to both these sets of questions

lies in our understanding about the stars. The answer

to the ﬁrst question is that we are made of star dust.

The answer to the second is that we are powered by

starlight.

2.2 Brightness and Colour

The two most obvious properties of stars are their

brightness and colour. The distances to atleast some

of these stars could be found by measuring their par-

allax. This meant that astronomers could plot the true

brightness of stars against their colours and what they

saw was that a pattern did emerge. The brighter stars

appeared bluish whereas the dimmer stars appeared

reddish. This is somewhat opposite to our everyday

notion of warm colour (which would be reddish) and

cool colour (which would be bluish). This plot of

brightness against colour is called the H-R diagram.

It is in trying to explain this diagram that a lot of our

knowledge about stars have emerged.

2.3 Physics of Stars

There are a lot of interesting facts and myster-

ies surrounding stars that are interesting to the average

physicist. Let us try to enumerate some of those. A star

is often deﬁned as a self gravitating object in which nu-

clear reactions are suﬃcient to balance radiative losses.

By self-gravitating we can understand that it is held to-

gether by it’s own gravity. But this leaves us with many

questions. Why doesn’t the Earth become a star? What

powers stars were a mystery until the discovery of nu-

clear energy. Even then it was not properly understood

until the discovery of quantum mechanics. Why was

this the case? It is interesting to note that even af-

ter nuclear energy reaction starts in the star, it would

take millions of years until the star starts to glow.Why

is this? The extreme conditions inside the star would

REFERENCES

Figure 1: Hot ember cools by radiating heat to it’s

surroundings.

mean that matter cannot remain in the ordinary state.

Instead the matter in the star is mostly in the form of

plasma. What complications does this bring along?

2.3.1 Cold Gas

We expect the situations in outer space to be cold.

How close do you have to go near the Sun to be heated

up? What happens when you have some gas in space?

One might expect the gas to become colder until it

reaches the same temperature as that of the surrounding

space. Can we say that the gas should disperse? This

what happens for example with hot ember. It cools

down to the temperature of the surroundings.

But in the case of stars, they are born in these

places with cold gas called nebulae. More correct

might be to use molecular clouds? How can that hap-

pen? How can these huge furnaces that generate so

much heat and light be born from these cold clouds of

gas?

2.3.2 Nuclear Energy

The Sun produces so much energy on a day to

day basis that we are left wondering what could be

the source of this energy. Could it be something that

caught ﬁre a long time ago and is still burning? Now

we know that the answer is controlled thermonuclear

fusion. Nuclear energy was only known as a source

of energy after the works of Einstein and others. But

even then we had to wait until the discovery of quantum

mechanics and quantum tunnelling to emphatically say

that this was the reason.Conﬁrmation of this theory

Figure 2: Sketch showing quantum tunnelling.

Figure 3: Blobs of plasma can fall like rain in the

sun’s atmosphere, as seen in the center of this image of

a solar ﬂare from July 2012. Image Credit: NASA

comes from observations of neutrinos

2.3.3 Plasma

What do you expect the star to be made of ? Is

it a solid orb made of metal? Is it gas or liquid? The

answer is neither. The matter in stars exist in the fourth

state of matter called plasma. However the plasma

can still be considered to behave like a perfect gas.

Talk about presence of magnetic ﬁelds. However since

plasma is composed of charged particles, the motion

of plasma can create magnetic ﬁelds. It is believed that

the magnetic ﬁeld in the stars are reponsible for the

heating up of their atmospheres and for the transient

events like ﬂares and CMEs.

References

[1] Arnab Rai Choudhuri. Nature’s Third Cycle: A

Story of Sunspots. Oxford University Press, Oxford

; New York, 2015. ISBN 978-0-19-967475-6.

[2] Frank H. Shu. The Physical Universe: An In-

troduction to Astronomy. A Series of Books in

REFERENCES

Astronomy. Univ. Sience Books, Sausalito, Calif,

9. print edition, 1982. ISBN 978-0-935702-05-7.

[3] Eric Priest. Magnetohydrodynamics of The Sun.

About the Author

Linn Abraham is a researcher in Physics,

specializing in A.I. applications to astronomy. He is

currently involved in the development of CNN based

Computer Vision tools for prediction of solar ﬂares

from images of the Sun, morphological classiﬁca-

tions of galaxies from optical images surveys and ra-

dio galaxy source extraction from radio observations.

The Hertzsprung-Russell Diagram and

Colour-Magnitude Diagram of the Pleiades

by Sindhu G

airis4D, Vol.2, No.10, 2024

www.airis4d.com

3.1 Introduction

The Hertzsprung-Russell (H-R) diagram and the

Colour-Magnitude Diagram (CMD) are crucial tools

in astrophysics for analyzing the characteristics of star

clusters, such as the Pleiades, a nearby and prominent

open star cluster. These diagrams oﬀer important in-

sights into stellar evolution, as well as the age and

composition of star clusters, enabling astronomers to

unravel the history of our galaxy.

This article will delve into the H-R diagram and

CMD, with a particular emphasis on their use in study-

ing the Pleiades. We will explore the theoretical foun-

dations of these diagrams, their interrelation, and how

astronomers utilize them to analyze stellar populations.

Lastly, we will highlight the distinctive features of the

Pleiades, using these diagrams to better understand its

stars.

3.2 The Hertzsprung-Russell (H-R)

Diagram

The H-R diagram is a scatter plot that represents

stars based on their luminosity (or absolute magnitude)

on the vertical axis and their surface temperature (or

spectral class) on the horizontal axis. This diagram

is one of the most powerful tools for visualizing stel-

lar evolution and comprehending the characteristics of

stars at diﬀerent stages of their life cycles.

Key Elements of the H-R Diagram:

Main Sequence: Most stars, including the Sun,

are located along a diagonal band called the main se-

quence. In this phase of their life cycle, they are ac-

tively fusing hydrogen in their cores.

Giant Branch: Stars that have exhausted the

hydrogen in their cores leave the main sequence and

evolve into red giants or supergiants, positioned in the

upper-right part of the diagram.

White Dwarfs: These faint, hot stars are found

in the lower-left area of the diagram. White dwarfs are

the remnants of stars that have shed their outer layers.

The H-R diagram provides a way to classify stars

and track their evolutionary progress, from their for-

mation as main sequence stars to their ultimate demise

as white dwarfs or supernova remnants.

3.3 The Colour-Magnitude Diagram

(CMD)

The Colour-Magnitude Diagram (CMD) is com-

parable to the H-R diagram, but instead of temperature,

it plots a star’s color (which serves as an indicator of

temperature) on the horizontal axis and its apparent or

absolute magnitude (brightness) on the vertical axis.

A star’s color is typically represented as the diﬀerence

in brightness between two ﬁlters, such as B and V, pro-

viding astronomers with a straightforward method to

estimate a star’s temperature.

3.5 The H-R Diagram of the Pleiades

Figure 1: Pleiades, identikit of the brightest star clus-

ter. (Image Credit: Wikipedia)

Key Components of the CMD:

Color: A star’s color indicates its temperature.

Hotter, more massive stars appear bluer, while cooler,

less massive stars appear redder.

Magnitude: Magnitude refers to a star’s bright-

ness. The CMD usually employs absolute magnitude,

representing the star’s intrinsic brightness, unaﬀected

by its distance from Earth.

The CMD is particularly valuable for studying star

clusters like the Pleiades, as it enables astronomers to

map the distribution of stars based on their brightness

and temperature. In contrast to the H-R diagram, which

relies on temperature, the CMD utilizes observable

parameters like apparent magnitude and color, making

it more straightforward to apply directly to star clusters.

3.4 The Pleiades

The Pleiades(Figure: 1), often referred to as the

Seven Sisters, is an open star cluster situated in the

constellation Taurus. It is one of the closest star clus-

ters to Earth, located roughly 444 light-years away.

The Pleiades comprises over 1,000 stars, although only

about seven are visible to the naked eye. As a relatively

young cluster, estimated to be around 100 million years

old, it serves as an excellent target for studying the early

stages of stellar evolution.

Characteristics of the Pleiades:

Age: Approximately 100 million years.

Distance: About 444 light-years (136 parsecs)

from Earth.

Composition: The cluster contains a mix of B-

type main-sequence stars and fainter, lower-mass stars.

Nebulosity: The cluster is surrounded by a faint

reﬂection nebula, which is most visible in long-exposure

photographs.

3.5 The H-R Diagram of the Pleiades

For the Pleiades, the H-R diagram illustrates that

most stars are found in the main sequence region, where

they are undergoing hydrogen fusion into helium. The

upper left portion of the diagram is occupied by hot,

massive blue stars, while cooler red stars are located

in the lower right. The main-sequence turnoﬀ point

serves as an indicator of the cluster’s age; for the

Pleiades, this point occurs at a color index of approxi-

mately B −V = −0.1, indicating that many stars have

recently commenced their hydrogen fusion phase.

The placement of stars on the H-R diagram oﬀers

valuable insights into their evolutionary stages. For

example, as massive stars deplete their hydrogen fuel,

they move oﬀ the main sequence and become red gi-

ants. In contrast, lower-mass stars can stay on the main

sequence for much longer durations. The Pleiades clus-

ter is considered relatively young, with an estimated

age of around 100 million years, as evidenced by the

absence of evolved red giants.

3.6 The Colour-Magnitude Diagram

of the Pleiades

When plotting the CMD for the Pleiades(Figure:

2), a distinct main sequence is evident, where the major-

ity of stars are located. The brightest stars correspond

to B-type stars at the top left, while cooler M-type stars

are found at lower magnitudes. The distance modu-

lus—deﬁned as m −M = 5 log





remains consis-

tent for all stars in this cluster because they share the

same distance.

The CMD also facilitates the analysis of stellar

populations within the cluster. By comparing the di-

3.8 Comparative Analysis with Other Clusters

Figure 2: Pleiades, identikit of the brightest star clus-

ter. (Image Credit: astro.rug.nl)

agram with theoretical models, researchers can ascer-

tain ages and evolutionary stages. The presence of

blue main-sequence stars suggests recent star forma-

tion activity within molecular clouds that supplied the

material for star formation. Additionally, some of the

fainter M-type stars may still be in the process of tran-

sitioning onto the main sequence.

3.7 Comparing the H-R Diagram and

CMD

While both the H-R diagram and CMD provide in-

sights into stellar evolution, they each have distinct ad-

vantages and limitations. The H-R diagram establishes

a more direct link to theoretical models by plotting sur-

face temperature, a fundamental parameter for stars. In

contrast, the CMD utilizes observable quantities such

as color and apparent magnitude, making it easier to

create with data from ground-based observations.

The CMD is particularly beneﬁcial for studying

open clusters like the Pleiades, where distance uncer-

tainties are minimal. Its color-magnitude relationship

oﬀers a clear depiction of the cluster’s evolutionary

state. Moreover, CMDs are more eﬀective for compar-

ing stars across diﬀerent clusters or galaxies since they

employ consistent observational parameters across var-

ious regions of the sky.

3.8 Comparative Analysis with Other

Clusters

When comparing the Pleiades to other nearby

clusters like the Hyades, notable diﬀerences appear

in their CMDs and H-R diagrams. For example, al-

though both clusters feature B-type and A-type stars,

the Hyades is older and shows more evolved red giants.

This comparison underscores the evolutionary changes

in stellar populations over time and oﬀers valuable in-

sights into the star formation history of our galaxy.

3.9 Conclusion

The H-R and CMD of the Pleiades are invaluable

tools for investigating the properties and evolution of

this young open cluster. By examining the positions

of stars in these diagrams, astronomers can ascertain

the cluster’s age, mass distribution, and evolutionary

status. The Pleiades act as an important laboratory for

comprehending the formation and evolution of stars

and stellar clusters.

References:

Hertzsprung–Russell diagram

Interpreting the HR diagram of stellar clusters

Star Clusters

Open Clusters

Pleiades

The Pleiades – or 7 Sisters – known around the

world

HR Diagram, Star Clusters,and Stellar Evolu-

tionThe Pleiades: Facts about the ”Seven Sis-

ters” star cluster

Pleiades, identikit of the brightest star cluster

Pleiades

Colour - Magnitude Diagram for M 45 (Pleiades)

3.9 Conclusion

About the Author

Sindhu G is a research scholar in Physics

doing research in Astronomy & Astrophysics. Her

research mainly focuses on classiﬁcation of variable

stars using diﬀerent machine learning algorithms. She

is also doing the period prediction of diﬀerent types

of variable stars, especially eclipsing binaries and on

the study of optical counterparts of X-ray binaries.

Part III

Biosciences

Brachythemis contaminata as Bio-Indicators

of Water Pollution: A Study in Agricultural

Landscapes

by Geetha Paul

airis4D, Vol.2, No.10, 2024

www.airis4d.com

1.1 Introduction

Water pollution is a critical global issue that sig-

niﬁcantly impacts both natural ecosystems and hu-

man health. Factors such as industrial discharges,

agricultural runoﬀ, and untreated sewage contribute

to the degradation of aquatic environments, leading

to biodiversity loss and compromised water quality.

In this context, bioindicator species, organisms sensi-

tive to environmental changes, serve as essential tools

for ecological monitoring and assessment. One such

species, Brachythemis contaminata, commonly known

as the ditch jewel, has garnered attention as an eﬀec-

tive bioindicator for polluted water bodies. This drag-

onﬂy’s sensitivity to pollutants allows researchers to

gauge the health of aquatic ecosystems, providing valu-

able insights into the impacts of human activities on

water quality. The presence, abundance, and behaviour

of Brachythemis contaminata can reﬂect broader eco-

logical conditions, making it a key species for monitor-

ing environmental changes. This article delves into the

signiﬁcance of Brachythemis contaminata in ecolog-

ical monitoring, examining its responses to pollution

and its potential role in promoting sustainable water

management practices. By integrating ﬁndings related

to this species, we can enhance our understanding of

aquatic health and develop strategies to mitigate the

eﬀects of water pollution (Simaika & Samways, 2009;

Kleyer et al., 2007).

1.2 Characteristics of Brachythemis

contaminata (Ditch jewel)

Brachythemis contaminata belongs to the family

Libellulidae within the order Odonata, which includes

dragonﬂies and damselﬂies. They are commonly called

Ditch jewel. As adults, they are relatively small drag-

onﬂies with broad, translucent wings and distinctive

dark patches near the wingtips. Males are more deeply

coloured than the females. They are commonly found

in tropical and subtropical regions across Asia and

Africa.

Figure 1: The external morphology of Brachythemis

contaminata male and female

1.3 Tolerance to Polluted Water Bodies

Figure 2: The images illustrate the Brachythemis con-

taminata congregations where high levels of pesticides

are ﬂoating in the water.

Photographs taken in contaminated environments

often show high concentrations of Brachythemis con-

taminata perching on reeds and other vegetation near

polluted water bodies, further illustrating their toler-

ance for such conditions. This species is highly adapt-

able and thrives in disturbed environments, including

habitats aﬀected by human activities such as agricul-

ture and urbanisation. Unlike many other Odonate

species, which prefer clean, oxygenated waters, Brachythemis

contaminata can tolerate moderately to heavily pol-

luted water bodies, making it a valuable subject for

environmental studies.

1.3 Tolerance to Polluted Water

Bodies

One of the primary reasons Brachythemis contam-

inata is considered a bio-indicator of water pollution is

its high tolerance to pollutants. In various studies, the

species has been observed to dominate in aquatic en-

vironments where water quality is poor. This includes

water bodies contaminated with fertilisers, pesticides,

and other chemical pollutants commonly associated

with agricultural runoﬀ.

For example, research has shown that Brachythemis

contaminata populations increase signiﬁcantly in ponds

and streams with high levels of nitrates and phosphates;

common pollutants from agricultural practices (Sel-

varasu et al., 2019. These chemicals contribute to eu-

trophication, a process that depletes oxygen levels and

harms most aquatic organisms. However, Brachythemis

contaminata appears to tolerate such conditions, allow-

ing it to thrive where other species might decline or

disappear.

1.4 Correlation with Agricultural

Runoﬀ

In regions with intensive agriculture, the use of

chemical fertilisers, herbicides, and insecticides leads

to runoﬀ that contaminates nearby water bodies. Sev-

eral studies have documented that the presence of Brachythemis

contaminata is often positively correlated with these

agricultural pollutants.

1.6 Comparisons with Other Odonates

Figure 3: Pie chart showing the increased percentage

of Brachythemis contaminata sps. in a study conducted

in Kuttanad Paddy Field.

A study by Shukla et al. (2016) found that Brachythemis

contaminata was abundant in water bodies near paddy

ﬁelds, where high levels of nitrogen-based fertilisers

were used. The presence of this species in polluted wa-

ter bodies, where dissolved oxygen levels are reduced

due to chemical pollutants, strongly indicates its adapt-

ability to degraded water quality. This adaptability

contrasts with more sensitive Odonates that disappear

when oxygen levels drop or when the water becomes

heavily contaminated.

1.5 Water Quality Parameters

Linked to Brachythemis

contaminata Presence

Brachythemis contaminata’s role as a bio-indicator

is reinforced by the correlation between its population

density and speciﬁc water quality parameters. Studies

measuring water quality where Brachythemis contam-

inata populations are abundant typically reveal poor

conditions, including elevated nitrate and phosphate

levels. These nutrients, often derived from fertilisers,

contribute to eutrophication.

High Biochemical Oxygen Demand (BOD): Wa-

ter bodies with high BOD indicate organic pollution,

which consumes oxygen, making it diﬃcult for many

aquatic species to survive.

Low Dissolved Oxygen (DO): Despite low oxygen

levels in polluted waters, Brachythemis contaminata

continues to thrive, making its presence an indicator of

oxygen depletion in water bodies.

Statistical analysis in several studies has conﬁrmed

a negative correlation between dissolved oxygen lev-

els and Brachythemis contaminata populations. This

further supports the idea that their prevalence in an

ecosystem signals poor water quality (Shukla et al.,

2016), (Selvarasu et al., 2019). The use of photo-

graphic evidence and statistical analysis has provided

further proof of Brachythemis contaminata’s status as a

bioindicator. By monitoring the species over time and

comparing its population dynamics with water qual-

ity measurements, researchers have consistently found

that its numbers increase as pollution worsens.

1.6 Comparisons with Other

Odonates

While many Odonate species, such as damselﬂies

and certain dragonﬂies, are highly sensitive to water

quality and prefer clean, oxygenated water, Brachythemis

contaminata stands out due to its ability to colonise

degraded habitats. This ability has led researchers to

classify it as a pollution-tolerant species, often con-

trasting it with more sensitive species like Coenagri-

onidae (damselﬂies) and other dragonﬂies from the

Gomphidae family, which disappear from polluted wa-

ter bodies (Arjun et. al., 2016).

This diﬀerential response among Odonates high-

lights the importance of species-level monitoring in

water quality assessments. The presence of Brachythemis

contaminata, in contrast to the absence of more sensi-

tive species, provides a clear signal of declining water

quality.

1.7 Conclusion

Brachythemis contaminata plays a critical role as a

bio-indicator of water pollution, particularly in ecosys-

tems impacted by agricultural runoﬀ and other sources

of chemical contamination. Its ability to thrive in pol-

luted water bodies, where other species struggle, makes

it an invaluable species for environmental monitoring.

Through a combination of ﬁeld observations, wa-

ter quality testing, and statistical analysis, numerous

studies have conﬁrmed the signiﬁcant correlation be-

tween Brachythemis contaminata populations and de-

graded water quality. Its presence in large numbers,

especially in water bodies with high levels of nutrients,

low dissolved oxygen, and high biochemical oxygen

1.7 Conclusion

demand, is a reliable indicator of aquatic pollution.

Incorporating bio-indicator species like Brachythemis

contaminata, into water quality monitoring programs

oﬀers a cost-eﬀective, eﬃcient, and ecological means

of assessing and managing environmental health. Mov-

ing forward, increased focus on such species could

enhance eﬀorts to mitigate water pollution and pro-

tect vulnerable ecosystems.This general overview illus-

trates the critical importance of Brachythemis contami-

nata in understanding and managing aquatic pollution.

By utilising these indicators, conservationists and en-

vironmental agencies can better address the challenges

posed by water contamination and take appropriate ac-

tions to improve water quality in aﬀected areas.

References

1. https://www.ijcrt.org/papers/IJCRT24A5742.pdf

2. Selvarasu, P., & Gunasekaran, C., et al. (2019).

Diversity of Odonates (Insecta: Odonata) in Dif-

ferent Habitats of Vellore District, Tamil Nadu,

India in Eastern Ghats. International Journal

of Recent Scientiﬁc Research, 10(04), 32127-

32130.

3. http://www.recentscientiﬁc.com/diversity-odonates-

insecta-odonata -diﬀerent-habitats-vellore-district-

tamil-nadu-india-eastern

4. Shukla, A., et al. (2016). International Journal

of Advances in Scientiﬁc Research, 2(04), 089-

093.

5. Simaika, J.P. and Samways, M.J. (2009) An Easy-

to-Use Index of Ecological Integrity for Priori-

tising Freshwater Sites and for Assessing Habi-

tat Quality. Biodiversity and Conservation, 18,

1171-1185.

6. https://doi.org/10.1007/s10531-008-9484-3

7. Resh, Vincent & Jackson, John. (1993). Rapid

Assessment Approaches to Biomonitoring Us-

ing Benthic Macroinvertebrates.

About the Author

Geetha Paul is one of the directors of

airis4D. She leads the Biosciences Division. Her

research interests extends from Cell & Molecular Bi-

ology to Environmental Sciences, Odonatology, and

Aquatic Biology.

Multi-Omics: A New Era in Biomarker

Discovery

by Jinsu Ann Mathew

airis4D, Vol.2, No.10, 2024

www.airis4d.com

In the previous article, we discussed the impor-

tance of biomarkers in improving our understanding of

health and disease, which helps create more personal-

ized ways to diagnose and treat patients. As the ﬁeld of

biomarker discovery evolves, we are now seeing a sig-

niﬁcant shift with the rise of multi-omics approaches.

The term ”omics” refers to the study of various bi-

ological molecules and their interactions, including

genomics, proteomics, transcriptomics, epigenomics,

and metabolomics. Traditionally, researchers have fo-

cused on single omics methods to ﬁnd biomarkers for

diagnosing and treating diseases. While these meth-

ods have provided useful insights, they often miss the

complexity and connections within biological systems.

Therefore, a more integrated approach is necessary to

fully understand the diverse nature of health and dis-

ease.

In this article, we will explore each omics layer

in detail, looking at how they contribute to our un-

derstanding of biological processes. The multi-omics

approach combines multiple layers of biological data,

allowing researchers to gain a fuller picture of the

molecular mechanisms behind health and disease. By

analyzing these diﬀerent types of data together, we can

uncover important interactions and patterns that might

be overlooked when examining each layer alone. This

comprehensive view not only helps us identify more

reliable biomarkers but also provides valuable insights

that can guide the development of targeted treatments

in precision medicine. Through multi-omics, we are

better equipped to understand the complexity of bio-

logical systems and improve patient care.

2.1 Genomics

Genomics is the study of the complete set of

DNA within an organism, commonly referred to as

its genome. In the context of multi-omics, genomics

serves as one of the foundational layers that help sci-

entists understand the biological basis of various traits,

diseases, and responses to treatments. Genomics fo-

cuses on identifying variations in DNA sequences,

such as single nucleotide polymorphisms (SNPs), in-

sertions, deletions, and mutations. These genetic vari-

ations can serve as biomarkers for diagnosing diseases,

predicting disease risk, or assessing how individuals

may respond to treatment. Genomic data provides the

underlying information on genes, which is essential for

understanding gene regulation. It shows how genes are

turned on or oﬀ in diﬀerent conditions, aﬀecting how

cells behave. In combination with other omics lay-

ers (like transcriptomics), genomics helps decode how

these changes impact protein production and cellular

functions. One of the key applications of genomics in

multi-omics is its role in personalized medicine. By

understanding a person’s unique genetic makeup, clin-

icians can develop tailored treatments that maximize

eﬃcacy and minimize side eﬀects. Genomics is instru-

mental in identifying genes associated with suscepti-

bility to complex diseases like cancer, cardiovascular

diseases, and neurological disorders. When genomics

is combined with other omics data (e.g., proteomics

2.4 Epigenomics

and metabolomics), researchers can explore how ge-

netic predispositions inﬂuence disease progression at

multiple molecular levels.

2.2 Proteomics

Proteomics, in the context of multi-omics, is the

large-scale study of proteins, which are the key func-

tional molecules in living organisms. Proteins carry

out most of the biological functions and serve as the

primary link between the genetic code (genome) and

cellular processes. Proteomics aims to identify, quan-

tify, and characterize the entire set of proteins (pro-

teome) expressed by an organism, tissue, or cell at

a speciﬁc time. In multi-omics studies, proteomics

complements other omics ﬁelds like genomics and

metabolomics by providing a direct view of biological

activities. While genomics and transcriptomics pro-

vide information on potential biological processes, pro-

teomics shows which proteins are actually produced,

how they are modiﬁed, and how they interact in a cel-

lular context. Proteomics can measure the expression

levels of proteins under diﬀerent conditions, identi-

fying how gene regulation translates into functional

molecules. Proteomics reveals modiﬁcations that pro-

teins undergo after translation (e.g., phosphorylation,

glycosylation), which are crucial for protein function,

signaling, and interactions. It helps identify how pro-

teins interact with each other to form complex net-

works, contributing to various cellular functions and

pathways. In the context of diseases, proteomics can

identify protein-based biomarkers that help in diagnos-

ing diseases, understanding pathophysiological mech-

anisms, and developing therapeutic strategies. Proteins

are more dynamic than genes or metabolites, constantly

changing in response to cellular states or environmen-

tal stimuli. Proteomics tracks these changes, providing

a more functional perspective on the biological pro-

cesses.

2.3 Transcriptomics

Transcriptomics is the study of the complete set of

RNA transcripts produced by the genome in a speciﬁc

cell or tissue, at a given time, under certain condi-

tions. Transcriptomics measures the levels of mes-

senger RNA (mRNA), which reﬂects which genes are

being actively transcribed and how they may respond

to certain stimuli or conditions. Combining transcrip-

tomics with other omics layers enables a more com-

prehensive view of biological processes. For instance,

genomics reveals genetic variations, while transcrip-

tomics shows how these variations aﬀect gene expres-

sion, linking genotype to phenotype.

Transcriptomics helps identify gene expression

patterns related to diseases, providing clues for di-

agnostics or treatment strategies. For example, tran-

scriptome changes in cancer cells can uncover speciﬁc

pathways involved in tumor growth. By studying non-

coding RNAs like microRNAs and long non-coding

RNAs, transcriptomics can reveal regulatory mecha-

nisms that control gene expression, contributing to a

deeper understanding of gene regulation in health and

disease. Transcriptomics is highly dynamic, reﬂecting

real-time changes in the cellular environment, allowing

scientists to observe how external factors like drugs or

environmental stress aﬀect gene expression.

2.4 Epigenomics

Epigenomics refers to the study of the complete

set of epigenetic modiﬁcations on the genetic material

of a cell. Unlike genomics, which focuses on the DNA

sequence itself, epigenomics looks at changes that reg-

ulate gene expression without altering the underlying

DNA sequence. These changes include DNA methy-

lation, histone modiﬁcations, and non-coding RNA

mechanisms, which together control gene activity and

play a crucial role in processes like development, dis-

ease progression, and environmental response.

In the context of multi-omics, epigenomics adds

a layer of regulation that bridges the gap between the

genome and downstream molecular processes, such as

gene expression (transcriptomics) and protein produc-

tion (proteomics). It helps provide a more compre-

hensive understanding of how genes are regulated in

response to environmental factors, lifestyle choices,

aging, and diseases. Epigenetic modiﬁcations control

2.6 Summary of Omic Levels

when and where genes are expressed, oﬀering insights

into how genes are switched on or oﬀ in diﬀerent con-

texts, like tissue types or disease states. Epigenomic

changes are often implicated in diseases such as cancer,

where abnormal DNA methylation patterns can silence

tumor suppressor genes. Integrating epigenomics with

genomics and proteomics helps identify biomarkers

for early diagnosis and potential therapeutic targets.

Epigenomics helps capture how external factors such

as diet, stress, toxins, or exposure to pollutants im-

pact gene expression, providing a clearer picture of the

gene-environment interaction in health and disease.

2.5 Metabolomics

Metabolomics in Multi-Omics refers to the large-

scale study of small molecules, or metabolites, within

cells, bioﬂuids, tissues, or organisms. Metabolomics is

the scientiﬁc study of the complete set of metabolites

(end products of cellular processes) present in a biolog-

ical sample. Metabolites are small molecules such as

sugars, lipids, amino acids, and other compounds that

represent the biochemical activities in cells. Unlike

the genome or proteome, which remains fairly con-

stant, the metabolome is highly dynamic and changes

in response to environmental conditions, disease states,

or drug treatments.

The role of metabolomics in multi-omics is par-

ticularly prominent in biomarker discovery. Since

metabolites are directly involved in biochemical reac-

tions, their levels can change more rapidly in response

to disease or treatment than gene or protein levels, of-

fering earlier detection and more sensitive monitoring

of biological changes. This is especially valuable in

areas like precision medicine, where detecting subtle

metabolic shifts can help tailor treatments to individual

patients. However, metabolomics also presents some

challenges when integrated into multi-omics studies.

The diversity and complexity of metabolites, coupled

with their rapid ﬂuctuation based on diet, environment,

and other factors, make data interpretation more diﬃ-

cult. Additionally, integrating metabolomics data with

genomic, proteomic, and transcriptomic information

requires sophisticated computational tools and meth-

(Image courtesy:https://www.researchgate.net/ﬁgure/Diﬀerent-main-levels-of-omics-technology-

for-evaluation-of-comprehensive-molecules-in ﬁg1 350668866)

Figure 1: Overview of the ﬁve key levels of -

omics technologies: Genomics, Epigenomics, Tran-

scriptomics, Proteomics, and Metabolomics.

ods to interpret the vast amount of information. De-

spite these challenges, metabolomics adds an invalu-

able layer of depth to multi-omics research, providing

key insights into how cellular functions operate across

diﬀerent biological levels. When combined, these lay-

ers of information oﬀer a comprehensive understand-

ing of how genotype leads to phenotype, elucidating the

molecular mechanisms that underlie health and disease.

2.6 Summary of Omic Levels

The main levels of “-omics” technologies allow

for a comprehensive evaluation of various molecules

within cells (1). Genomics focuses on genetic vari-

ants in the DNA sequence, while Epigenomics exam-

ines non-DNA sequence modiﬁcations such as histone

changes and methylation. Transcriptomics analyzes the

expression and structural alterations in RNA, includ-

ing variants like splice sites, and Proteomics assesses

protein expression, modiﬁcations, and overall interac-

tions. Finally, Metabolomics describes the functional

metabolites within cells. The integration of these dif-

ferent “-omics” technologies provides valuable insights

that can enhance the diagnosis, prognosis, and thera-

peutic approaches for diﬀerent diseases.

2.7 Conclusion

In conclusion, multi-omics represents a transfor-

mative approach in biological research and medicine,

2.7 Conclusion

oﬀering a comprehensive understanding of cellular

processes by integrating data from genomics, epige-

nomics, transcriptomics, proteomics, and metabolomics.

This holistic view enables researchers to map the com-

plex interactions between genes, proteins, and metabo-

lites, providing deeper insights into the molecular mech-

anisms underlying health and disease. In the context of

precision medicine, multi-omics holds immense poten-

tial for identifying novel biomarkers, improving disease

diagnosis and prognosis, and developing targeted ther-

apeutic strategies. As technology advances and data in-

tegration methods improve, multi-omics will continue

to revolutionize our understanding of biology and pave

the way for more personalized and eﬀective healthcare

solutions.

References

Multi-omics approaches to disease

A guide to multi-omics

Applications of multi-omics analysis in human

diseases

Multiomics oﬀers a holistic view of biology

What is Multiomics?

State of the Field in Multi-Omics Research: From

Computational Needs to Data Mining and Shar-

ing

An overview of -omics technologies in multi-

omics

About the Author

Jinsu Ann Mathew is a research scholar

in Natural Language Processing and Chemical Infor-

matics. Her interests include applying basic scientiﬁc

research on computational linguistics, practical appli-

cations of human language technology, and interdis-

ciplinary work in computational physics.

Part IV

Remote Sensing

Promise of Synthetic Aperture Radars for

Remote Sensing

by Balamuralidhar P

airis4D, Vol.2, No.10, 2024

www.airis4d.com

Synthetic Aperture Radar (SAR) has already proven

as a powerful imaging technology for remote sensing

applications. It uses active illumination using radio

waves to image a target area and is mounted on a

moving platform such as satellite or drone. Synthetic

Aperture is a technique to improve eﬀective resolution

beyond the physical aperture of the Radar by utilizing

the smart acquisition and spatiotemporal processing of

the reﬂected signals.

A radar antenna typically has limited resolution

due to its size. Larger antennas provide higher res-

olution, but space constraints make this diﬃcult for

satellites. SAR overcomes this limitation by simulat-

ing a large antenna using the motion of the satellite.

As the satellite moves in its orbit, it transmits multi-

ple pulses from diﬀerent positions. The radar system

combines these multiple radar signals, collected over

time, to simulate a large synthetic aperture or antenna,

greatly improving the resolution of the image. This

process creates a detailed image as if it were captured

by a much larger antenna than the satellite physically

carries.

As the satellite moves, the radar pulses experience

a Doppler shift due to the relative motion between the

satellite and the target on the ground. By measuring

this Doppler shift, SAR can diﬀerentiate between sig-

nals coming from diﬀerent points on the ground. This

helps create a 2D image with high spatial resolution,

both in range (distance from the satellite) and in az-

imuth (direction along the satellite’s path). In addition

to received signal strength, the phase is also measured.

Since it uses microwaves, SAR is unaﬀected by

weather conditions or sunlight, providing continuous

monitoring. This has a strong advantage of operat-

ing 24/7 unlike optical imaging. Depending on the

frequency, SAR can penetrate vegetation, ice, and in

some cases, dry soil, oﬀering a glimpse below surface

layers. SAR can image large swaths of land while still

oﬀering high-resolution details.

With interferometry and polarimetry mode of op-

erations, SAR can give information on high resolution

changes and precise material detection / classiﬁcation.

1.1 Disaster Management and

Climate Change Monitoring

SAR’s ability to penetrate cloud cover makes it

ideal for ﬂood mapping and disaster prediction, pro-

viding accurate real-time data to governments and aid

organizations for more eﬀective response strategies.

SAR imagery is critical in monitoring polar ice

caps, glaciers, and snowpacks. In the face of climate

change, this will be essential for tracking rising sea

levels, water resources, and global warming trends.

SAR has proven to be eﬀective in several remote

sensing applications including:

Precision monitoring of infrastructure: SAR can

detect millimeter-level changes in structures like

bridges, dams, and buildings, helping prevent

disasters by identifying weak points before they

fail. This will be key for smart cities and infras-

1.1 Disaster Management and Climate Change Monitoring

Figure 1: This SAR image captured by Capella Space

clearly show the moment a cargo ship collided with

Baltimore’s Francis Scott Key Bridge in March 2024

(Credit: Capella Space)

tructure resilience.

Urban sprawl and planning: Governments and

planners can use SAR to monitor urban expan-

sion, track illegal construction, and plan sustain-

able development more eﬀectively.

Precision agriculture: SAR data combined with

AI can monitor soil moisture, vegetation health,

and crop yield predictions, helping farmers opti-

mize resource usage and improve sustainability.

Deforestation and environmental conservation:

SAR’s ability to penetrate vegetation allows for

continuous forest monitoring, even in heavily

forested or cloudy areas, aiding in the preven-

tion of illegal logging and environmental degra-

dation.

Illegal ﬁshing and maritime tracking: SAR can

monitor vast ocean areas to detect illegal ﬁshing

activities, oil spills, and track ships that turn oﬀ

their transponders. This is crucial for protecting

marine resources and securing borders.

Environmental protection: SAR’s ability to track

oil spills and monitor sea ice will be important

for preserving marine ecosystems and mitigating

environmental damage.

SAR satellites generally used to be quite heavy and

costly. But recently smaller and cheaper payloads in

low earth orbits (LEO) are being deployed successfully.

Iceye, a Finnish startup and Capella Space, a US based

startup, operate several SAR satellites and they are

equipped to image any part of the world at a short

notice.

There are major challenges for SAR technology

to be used in satellites in a cost eﬀective manner and

that will determine its future. Since it is an active pay-

load, it will consume substantial power needing larger

batteries and solar panels. Also large size deployable

antennas required to achieve higher resolutions. This

could also be achieved through multiple satellites in a

constellation ( as a spatial array) however synchronis-

ing them is another major challenge. Meta materials

are being explored for a better antenna design. Com-

munication bandwidth has to increase substantially for

transporting the raw data to ground stations. High

performance lossless data compression algorithms can

help in data reduction. Onboard edge processing is

another option where majority of the data is processed

in satellite itself and only relevant information is sent

to ground station.

China has launched world’s ﬁrst geosynchronous

SAR satellite , L band 20m resolution in 2023. They

have several LEO SAR satellites already in orbit. Japanese

startup Synspective launched its ﬁrst SAR satellite re-

cently and are planning to increase the constellation.

A US based startup Umbra Space launched a bistatic

SAR satellite system where two satellites operate as a

coherent pair in tandem. They are planning to expand

it to a multistatic conﬁguration in near future. This

innovative approach opens the door to various appli-

cations, including Intelligence Surveillance Recogni-

zance (ISR) capability, elevation modeling, imaging

resilience, and the implementation of moving target

indication techniques.

In India, ISRO has launched RISAT and DS-

SAR satellites in orbit. NISAR satellite having multi-

band SAR being built with ISRO-NASA collaboration-

launch is delayed and expected sometime next year.

There are two startups in India, namely Galaxye and

Sisir Radar, planning to launch their powerful SAR

satellites in near future. They have drone based high

altitude SAR missions already.

One of the limitations of working with SAR data

has been the somewhat tedious preprocessing steps that

lower-level SAR data requires. Depending on the type

of analysis you want to do, these preprocessing steps

can include: applying the orbit ﬁle, radiometric cal-

1.1 Disaster Management and Climate Change Monitoring

Figure 2: Bistatic SAR image of a dam ( Credit Umbra

Space )

ibration, de-bursting, multilooking, speckle ﬁltering,

and terrain correction.

Applying orbit ﬁles deﬁnes the relationship be-

tween ground and image coordinates, improves accu-

racy of later orbit-based calibration steps. Radiometric

calibration converts the image pixel values from digi-

tal number to a standard geophysical measurement unit

of radar backscatter. SAR scenes can be made up of

multiple swaths or sections. De-bursting step com-

bines all swaths into a single image. Multilooking uses

spatial averaging to reduce image speckle noise and

converts to ground range, producing an image with a

standard pixel size. Speckle ﬁltering removes noise,

or speckle, in an image. Many types of speckle ﬁlters

can be applied, and diﬀerent applications have spe-

ciﬁc ﬁlters that may work best. This is followed by

two terrain correction operations. Radiometric Terrain

Flattening uses a digital elevation model (DEM) to

remove geometry-dependent radiometric distortions;

normalizes measured backscatter with respect to ter-

rain slope. Geocoding uses a DEM to remove geo-

metric distortions such as foreshortening, layover, and

shadow; connects the image to a geographic coordinate

system. Sometimes linearly-scaled data is converted

into decibels (dB). Specialised software is required to

process SAR data, depending on the data provider,

starting level of data, and desired level of data.

The preprocessed SAR data is used for geospatial

information extraction through advanced data process-

ing / analysis techniques. The information to be ex-

tracted can be in the following three major categories.

Mapping & Land classiﬁcation, Parameter retrieval

such as wind speed, soil moisture etc, and Object detec-

tion/recognition. Along with classical spatio-temporal

algorithms, deep neural network based techniques are

also increasingly used in SAR processing.

In spite of these advances, SAR automatic pro-

cessing is still challenging. The scattering mechanisms

are not well known and studies to combine physical and

empirical models are still needed. Advanced models

on multisource data, necessary to reduce uncertainty,

will be a focal point for future work.

In addition to currently deployed SAR satellites

there are many more planning to enter the orbit. That

means abundance of high quality SAR data will be

available for use in various applications. It is high time

more researchers to enter in this ﬁeld and use the oppor-

tunity to contribute in socially impactful applications.

References

1. Synthetic Aperture Rasar for Geosciences,

Lingscheng Meng et.al, Reviews in Geophysics, Vol-

ume 62, 2024

2. Advancements in On-Board Processing of Syn-

thetic Aperture Radar (SAR) Data: Enhancing Eﬃ-

ciency and Real-time Capabilities, Laura Parra Garcia,

IEEE JOURNAL OF SELECTED TOPICS IN AP-

PLIED EARTH OBSERVATIONS AND REMOTE

SENSING, MARCH 2024

3. Synspective’s Fifth SAR Satellite Success-

fully Reaches its Target Orbit and Spreads its Wings

REFERENCES

About the Author

Dr.Balamuralidhar Purushothaman is a

former Chief Scientist at TCS Research Bangalore.

Currently he is continuing in TCS Research as a re-

search advisor. He obtained his PhD from Aalborg

university Denmark, MTech from IIT Kanpur and

BTech from TKM Engg Quilon. His research inter-

est and contributions are in IoT, Sensor Informatics,

Remote Sensing and Robotics & AI. He has over 170

publications and 110 patents in these technology and

application areas. He has published a book on ‘IoT

Technical Challenges and Solutions’.

References

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.

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

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