Autorentext

Yogesh Chandra, PhD is an assistant professor of physics at the Government Post Graduate College, Bazpur, Kumaun University, India. He has published several journal articles, mentored many students, and attended a number of conferences and workshops. He specializes in astronomy, astrophysics, and atmospheric science, with a focus on AI applications in these fields. Manjuleshwar Panda is an independent astronomy researcher in New Delhi, India, with an M.Sc. in Physics from Kumaun University, Nainital, India. He has contributed to national and international research programs and has completed two specialized courses with the Indian Space Research Organization. He has a keen interest in observational and extragalactic astronomy, high-energy astrophysics, and the role of AI in astronomy. Mahesh Chandra Mathpal, PhD is a lecturer in physics at Govt. IC Lohali, Uttarakhand, India. He has published over ten research papers in international journals and is actively engaged in advancing AI-driven astrophysical studies. His research focuses on astrophysics and solar physics, with a specialization in applying artificial neural networks (ANN) to these fields.

Klappentext

Uncover the universe's secrets with this essential guide that provides a comprehensive exploration of how artificial intelligence is revolutionizing modern astronomical research. Artificial intelligence (AI) is revolutionizing astronomy, enabling researchers to process vast datasets, uncover hidden patterns, and enhance observational precision like never before. This book explores this transformative synergy, bringing together insights from experts across the globe. Covering a wide spectrum of topics, including AI-driven data mining, exoplanet discovery, gravitational wave detection, and autonomous observatories, this book highlights the impact of machine learning, computer vision, and big data analytics on modern astrophysical research. From detecting transient celestial events to refining cosmic evolution models, this volume delves into the ways AI is reshaping our understanding of the cosmos. As we enter a new era of discovery, this guide serves as both a foundational reference and a forward-looking exploration of AI's expanding role in space science. Whether you are a student, researcher in astronomy or space science, or an AI practitioner, this book offers an invaluable resource on the frontiers of AI-driven astronomical research. Readers will find this volume:

• Provides a balanced mix of fundamental concepts, practical applications, and future perspectives;
• Designed to be informative and approachable, combining scientific insights, high-quality images, and detailed analyses to enhance understanding;

• Explores how AI is transforming space exploration, telescope automation, and cosmic data processing, providing readers a future-focused perspective. Audience Academics, researchers, astronomers, astrophysicists, and industry professionals interested in the transformative power of AI for astrological applications.

  Inhalt

  Foreword xxv
  Preface xxvii
  Acknowledgement xxxi

  Part I: Foundations and Core Applications of AI in Astronomy 1

  1 Introduction to AI in Astronomy 3
  Rahul Barnwal, Aman Kumar, Kala S. and Sree Ranjani Rajendran

  1.1 Introduction 4
  1.2 Understanding AI: Key Concepts and Techniques 6
  1.3 Fundamentals of Deep Learning 8
  1.4 AI Algorithms Shaping Astronomical Research 14
  1.5 Revolutionizing Data Analysis: AI in Astronomical Surveys 18
  1.6 Machine Learning Models for Celestial Object Classification 21
  1.7 AI in Observational Astronomy: Transforming Telescopic Data 24
  1.8 Harnessing AI for Space Exploration and Planetary Science 26
  1.9 AI-Driven Discoveries: Case Studies in Astronomy 29
  1.10 Challenges and Limitations of AI in Astronomy 32
  1.11 The Future of AI in Astronomy: Opportunities and Horizons 34
  1.12 Conclusion 41

  2 Data Mining and Machine Learning in Astrophysics 47
  Gissmol Saji and Sanjay Singh Bisht

  2.1 Introduction 48
  2.2 Foundations of Data Mining and Machine Learning 50
  2.3 Machine Learning Applications in Astrophysics 55
  2.4 Role of Machine Learning in Key Astrophysical Research Areas 58
  2.5 Challenges in the Era of Big Data 75
  2.6 Bridging Observations and Theory 77
  2.7 The Future: Autonomous Observatories and Predictive Models 79
  2.8 Conclusion 81

  3 The Role of Artificial Intelligence in the Discovery and Characterization of Exoplanets 87
  Shraddha. Biswas, D. Bisht and Ing-Guey Jiang

  3.1 Introduction 88
  3.2 Exoplanet Discovery 89
  3.3 Naming Rules/Nomenclature 92
  3.4 Types of Exoplanets 92
  3.5 Detection Methods 98
  3.6 Missions Launched to Detect Exoplanets 112
  3.7 Role of Artificial Intelligence in Exoplanetary Science 117
  3.8 Conclusion 123

  4 Cosmology and Dark Matter Research 129
  Arun Kumar Rathore, B. C. Chanyal and Sirley Marques-Bonham

  4.1 Introduction 130
  4.2 Role of Dark Matter in the Cosmos 133
  4.3 Future Cosmological Observations 133
  4.4 Evidence of Dark Matter 134
  4.5 Theoretical Models of Dark Matter 149
  4.6 CDM and MOND 156
  4.7 Sterile Neutrinos 161
  4.8 Method of Direct Detection 163
  4.9 Indirect Detection 166
  4.10 Role of Artificial Intelligence in Dark Matter and Cosmology 169
  4.11 AI's Role in Quantum Simulations of Dark Matter 172
  4.12 Challenges and Future Prospects 172
  4.13 Enhancing Analysis and Interpretation of Astronomical Data 173
  4.14 AI in Theory Development and Hypothesis Generation 174
  4.15 Challenges and Future Prospects 174
  4.16 Conclusion 174

  5 Gravitational Wave Detection 181
  Muhammad Zeshan Ashraf, Farhat Shakeel and Tahira Saeed

  5.1 Introduction 182
  5.2 Gravitational Wave Observatories and Detection Techniques 185
  5.3 Multi-Messenger Astronomy and Astrophysical Sources 189
  5.4 Artificial Intelligence in Gravitational Wave Detection 192
  5.5 Challenges and Future Prospects 194
  5.6 Conclusion 197

  6 Harmonizing the Cosmos: Radio Astronomy and AI Integration 201
  Manjuleshwar Panda, Aadarsh Kumar Chaudhri and Mukesh Kumar Pandey

  6.1 Introduction: The Synergy of Radio Astronomy and AI 202
  6.2 Foundations of Radio Astronomy: Unlocking the Invisible Universe 204
  6.3 The Evolution of AI in Radio Astronomy 208
  6.4 AI-Powered Signal Processing: Detecting the Weakest Cosmic Signals 211
  6.5 Fast Radio Bursts and AI: Solving One of Astronomy's Biggest Mysteries 213
  6.6 AI in Pulsar and SETI Research: Searching for Cosmic Beacons 216
  6.7 AI in Very Long Baseline Interferometry and Image Reconstruction 219
  6.8 AI and Large Radio Surveys: Managing the Data Tsunami 223
  6.9 Future Prospects: AI and Next-Generation Radio Astronomy 226
  6.10 Conclusion: The Future of AI-Driven Radio Astronomy 229

  Part II: Advanced Techniques, Observatories, and Future Prospects 233

  7 Image Processing and Computer Vision in Astronomy 235
  Deepak Pandey, Garima Punetha and Chetna Tewari

  7.1 Introduction to Image Processing in Astronomy 236
  7.2 Applications of Image Processing in Astronomy 238
  7.3 Processing Techniques for Detecting Transient Events 247
  7.4 Specific Techniques for Detecting Key Transients 251
  7.5 Role of Computer Vision in Astronomy 255
  7.6 Advantages of Using Computer Vision in Astronomy 258
  7.7 Applications 261
  7.8 Challenges in Astronomical Image 263
  7.9 Challenges in Interpretability for Astronomy 268
  7.10 Future Directions 271
  7.11 Conclusion 272

  8 Astroinformatics and Big Data Challenges 279
  Kanthavel R., Adline Freeda R. and Dhaya R.

  8.1 Introduction to Astroinformatics 280
  8.2 Big Data in Astronomy 283
  8.3 Data Management in Astroinformatics 285
  8.4 Data Processing Techniques 292
  8.5 Data Visualization in Astroinformatics 295
  8.6 Statistical Challenges in Astroinformatics 301
  8.7 Time-Domain Astronomy 305
  8.8 Future Directio…