The Intelligent Universe : AI's Role in Astronomy.

Format:

Book

Author/Creator:

Chandra, Yogesh.

Contributor:

Panda, Manjuleshwar.

Mathpal, Mahesh Chandra.

Language:

English

Subjects (All):

Astronomy--Data processing.

Astronomy.

Physical Description:

1 online resource (516 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

Uncover the universe's secrets with this essential guide that provides a comprehensive exploration of how artificial intelligence is revolutionizing modern astronomical research.

Contents:

Cover

Series Page

Title Page

Copyright Page

Dedication Page

Contents

Foreword

Preface

Acknowledgement

Part I: Foundations and Core Applications of AI in Astronomy

Chapter 1 Introduction to AI in Astronomy

1.1 Introduction

1.2 Understanding AI: Key Concepts and Techniques

1.2.1 What is AI?

1.2.2 Machine Learning

1.2.3 Deep Learning

1.3 Fundamentals of Deep Learning

1.3.1 Building Blocks of a Neural Network

1.3.2 Training and Optimization

1.3.3 Addressing Challenges in Deep Learning

1.3.4 Training a Neural Network

1.4 AI Algorithms Shaping Astronomical Research

1.4.1 Convolutional Neural Network

1.4.2 Recurrent Neural Network

1.4.3 Long Short-Term Memory

1.4.4 Reinforcement Learning

1.5 Revolutionizing Data Analysis: AI in Astronomical Surveys

1.5.1 Popular Machine Learning Libraries

1.5.2 Astronomical Software and APIs

1.5.3 Public Datasets and Repositories

1.6 Machine Learning Models for Celestial Object Classification

1.6.1 Random Forest Model for Classifying Celestial Objects Into Three Categories: STAR, GALAXY, Or Quasi-Stellar Object

1.6.2 k-Nearest Neighbors for Classification of Celestial Objects Into Distinguishing AGNs from Stars and Galaxies

1.6.3 Convolutional Neural Network for Classifying Galaxies

1.7 AI in Observational Astronomy: Transforming Telescopic Data

1.7.1 AI-Powered Image Processing in Astronomy

1.7.2 Real-Time Data Processing and Automated Observations

1.7.3 AI in Telescope Data Compression and Storage

1.8 Harnessing AI for Space Exploration and Planetary Science

1.8.1 AI-Driven Autonomous Navigation for Deep Space Missions

1.8.2 AI for Exoplanet Discovery and Characterization

1.8.3 AI in Planetary Surface Exploration and Robotic Operations

1.9 AI-Driven Discoveries: Case Studies in Astronomy.

1.9.1 AI-Enhanced Gravitational Wave Detection

1.9.2 ML for Anomaly Detection in Astronomical Data

1.9.3 AI-Driven Meteor Shower Mapping

1.10 Challenges and Limitations of AI in Astronomy

1.10.1 Sources of Bias in Astronomical Data

1.10.2 Mitigating Bias in AI Applications

1.10.3 The Importance of Interpretability: Understanding How AI Models Make Decisions

1.10.4 Key Approaches to Interpretability

1.11 The Future of AI in Astronomy: Opportunities and Horizons

1.12 Conclusion

References

Chapter 2 Data Mining and Machine Learning in Astrophysics

2.1 Introduction

2.2 Foundations of Data Mining and Machine Learning

2.2.1 Data Science and Its Components

2.2.2 Classical Machine Learning Versus Deep Learning

2.2.3 Data Mining Software and Tools

2.3 Machine Learning Applications in Astrophysics

2.3.1 Supervised Learning Techniques

2.3.2 Unsupervised Learning Techniques

2.3.3 Structure of Training Data: Supervised and Unsupervised

2.3.4 Semi-Supervised Learning Techniques

2.4 Role of Machine Learning in Key Astrophysical Research Areas

2.4.1 Exoplanet Detection

2.4.2 Gravitational Wave Analysis

2.4.3 Galaxy Classification

2.4.4 Transient Event Identification

2.5 Challenges in the Era of Big Data

2.5.1 Managing Vast Data Volumes

2.5.2 Addressing Observational Noise and Data Imbalance

2.5.3 Enhancing Model Interpretability

2.6 Bridging Observations and Theory

2.6.1 Enhancing Theoretical Simulations with Observational Data

2.6.2 Revolutionizing Astrophysical Simulations

2.6.3 Processing and Analyzing Vast Astrophysical Datasets

2.6.4 Discovering New Astrophysical Phenomena

2.7 The Future: Autonomous Observatories and Predictive Models

2.7.1 Real-Time Event Detection and Response

2.7.2 Predictive Modeling in Astrophysics.

2.7.3 A New Era of Astrophysical Discovery

2.8 Conclusion

Data Availability

Chapter 3 The Role of Artificial Intelligence in the Discovery and Characterization of Exoplanets

3.1 Introduction

3.2 Exoplanet Discovery

3.3 Naming Rules/Nomenclature

3.4 Types of Exoplanets

3.4.1 Gas Giants

3.4.2 Hot Jupiters

3.4.3 Terrestrial Exoplanets

3.4.4 Super-Earths

3.4.5 Neptunian Exoplanets

3.4.6 Exo-Earths

3.4.7 Water World (Ocean Planets)

3.4.8 Chthonian Planets

3.4.9 Rogue Exoplanets

3.5 Detection Methods

3.5.1 Direct Detection

3.5.1.1 Imaging

3.5.2 Indirect Detection

3.5.2.1 Radial Velocity Tracking

3.5.2.2 Astrometry

3.5.2.3 Pulsar Timing

3.5.2.4 Transit Method

3.5.2.5 Gravitational Microlensing

3.6 Missions Launched to Detect Exoplanets

3.6.1 Kepler Space Telescope (2009-2018)

3.6.2 Transiting Exoplanet Survey Satellite (TESS) (2018-Present)

3.6.3 Hubble Space Telescope (1990-Present)

3.6.4 James Webb Space Telescope (2021-Present)

3.6.5 COROT (2006-2013)

3.6.6 PLATO (PLAnetary Transits and Oscillations of Stars) (Launch Expected 2026)

3.6.7 CHEOPS (CHaracterising ExOPlanet Satellite) (2019-Present)

3.6.8 Spitzer Space Telescope (2003-2020)

3.6.9 Gaia (2013-Present)

3.6.10 WISE (Wide-Field Infrared Survey Explorer) (2010-2011)

3.7 Role of Artificial Intelligence in Exoplanetary Science

3.7.1 Role of AI in Discovering and Studying Exoplanets

3.7.1.1 Data Analysis and Detection

3.7.1.2 Characterization of Exoplanets

3.7.1.3 Pattern Recognition and Anomaly Detection

3.7.1.4 Improving Telescope Operations

3.7.1.5 Optimization of Surveys

3.7.1.6 Collaborative Efforts with Simulations

3.7.2 Role of AI-Based Tools in Finding Habitable Planets.

3.7.2.1 Searching for Exoplanet Habitability Using a Novel Anomaly Detection Method

3.7.2.2 TOLIMAN Mission to Search for Habitable Worlds in the Alpha Centauri System

3.7.2.3 Machine Learning Techniques to Study the Internal Structure of Rocky Exoplanets

3.7.2.4 Contribution of Artificial Intelligence in Searching Exoplanets

3.8 Conclusion

Chapter 4 Cosmology and Dark Matter Research

4.1 Introduction

4.2 Role of Dark Matter in the Cosmos

4.3 Future Cosmological Observations

4.4 Evidence of Dark Matter

4.4.1 Mini-Galaxies

4.4.1.1 Rotation Pattern of Spiral Galaxies

4.4.1.2 Other Evidence At the Galactic Scale

4.4.2 Midi: Cluster of the Galaxies

4.4.2.1 Effect of Gravitational Lensing

4.4.3 The Universe: Maxi

4.4.3.1 Large-Scale Structure Formation

4.4.3.2 Cosmic Microwave Background

4.4.3.3 Big Bang Nucleosynthesis

4.5 Theoretical Models of Dark Matter

4.5.1 MAssive Compact Halo Objects

4.5.2 WIMPs

4.5.3 Lambda Cold Dark Matter

4.5.3.1 Extending the .CDM Model

4.5.4 MOdified Newtonian Dynamics

4.5.4.1 Observational Evidence of MOND

4.6 .CDM and MOND

4.6.1 Interpretation of the .CDM-MOND Debate

4.6.2 Justified Extension Explanation of Models

4.6.3 Axions

4.7 Sterile Neutrinos

4.7.1 Creation and Decay of Sterile Neutrinos

4.8 Method of Direct Detection

4.8.1 Rates, Spectra, and Interactions

4.9 Indirect Detection

4.9.1 Energy Spectrum at Production

4.9.2 Secondary Photons

4.10 Role of Artificial Intelligence in Dark Matter and Cosmology

4.10.1 Data Analysis and Pattern Recognition

4.10.2 Simulating Cosmological Models

4.10.3 Enhancing Observational Techniques

4.10.4 Deep Learning in Direct and Indirect Detection

4.10.5 Accelerating Theoretical Model Testing

4.10.6 Enhancing Survey Efficiency.

4.10.7 Accelerating Discoveries with AI-Driven Hypotheses

4.11 AI's Role in Quantum Simulations of Dark Matter

4.12 Challenges and Future Prospects

4.12.1 Toward a Dark Matter Breakthrough

4.12.2 Handling Vast and Complex Datasets

4.13 Enhancing Analysis and Interpretation of Astronomical Data

4.13.1 Advanced Cosmological Simulations

4.13.2 Direct and Indirect Detection Support

4.14 AI in Theory Development and Hypothesis Generation

4.15 Challenges and Future Prospects

4.16 Conclusion

Acknowledgment

Chapter 5 Gravitational Wave Detection

5.1 Introduction

5.1.1 Theoretical Foundations of Gravitational Waves

5.1.2 Early Indirect Evidence and Predictions

5.1.3 The Breakthrough of Direct Detection

5.2 Gravitational Wave Observatories and Detection Techniques

5.2.1 LIGO, Virgo, and KAGRA: Current Ground-Based Detectors

5.2.2 Space-Based Observatories: LISA and Beyond

5.2.3 Sensitivity and Noise Reduction Strategies

5.3 Multi-Messenger Astronomy and Astrophysical Sources

5.3.1 Neutron Star Mergers and Kilonovae

5.3.2 Black Hole Mergers and Event Horizon Studies

5.3.3 Exotic Sources and High-Energy Astrophysics

5.4 Artificial Intelligence in Gravitational Wave Detection

5.4.1 Machine Learning for Signal Processing

5.4.2 Deep Learning for Event Classification

5.4.3 AI-Driven Noise Filtering and Optimization

5.5 Challenges and Future Prospects

5.5.1 Fundamental Sensitivity Limits of Current Detectors

5.5.2 Next-Generation Observatories: Einstein Telescope and Cosmic Explorer

5.5.3 AI's Expanding Role in Future Gravitational Wave Research

5.6 Conclusion

Chapter 6 Harmonizing the Cosmos: Radio Astronomy and AI Integration

6.1 Introduction: The Synergy of Radio Astronomy and AI.

6.1.1 Overview of Radio Astronomy and Its Significance in Modern Astrophysics.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-35551-3

1-394-35550-5

1-394-35549-1

9781394355495

OCLC:

1543510960