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Submit a ProposalThe Intelligent Universe
 | AI's Role in Astronomy Edited by Yogesh Chandra, Manjuleshwar Panda and Mahesh Chandra Mathpal Copyright: 2026 | Expected Pub Date:2025/10/30 ISBN: 9781394355488 | Hardcover | 514 pages |
One Line Description
Uncover the universe’s secrets with this essential guide that provides a comprehensive exploration of how artificial intelligence is revolutionizing modern astronomical research.
Audience
Academics, researchers, astronomers, astrophysicists, and industry professionals interested in the transformative power of AI for astrological applications.
Academics, researchers, astronomers, astrophysicists, and industry professionals interested in the transformative power of AI for astrological applications.
Description
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:
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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.
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Author / Editor Details
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.
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. 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.
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Table of Contents
Foreword
Preface
Acknowledgement
Part I: Foundations and Core Applications of AI in Astronomy
1. Introduction to AI in Astronomy
Rahul Barnwal, Aman Kumar, Kala S. and Sree Ranjani Rajendran
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
2. Data Mining and Machine Learning in Astrophysics
Gissmol Saji and Sanjay Singh Bisht
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
Acknowledgement
References
3. The Role of Artificial Intelligence in the Discovery and Characterization of Exoplanets
Shraddha. Biswas, D. Bisht and Ing-Guey Jiang
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
Acknowledgement
References
4. Cosmology and Dark Matter Research
Arun Kumar Rathore, B. C. Chanyal and Sirley Marques-Bonham
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 ΛCDM Model
4.5.4 MOdified Newtonian Dynamics
4.5.4.1 Observational Evidence of MOND
4.6 ΛCDM and MOND
4.6.1 Interpretation of the ΛCDM–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
Data Availability
Acknowledgment
References
5. Gravitational Wave Detection
Muhammad Zeshan Ashraf, Farhat Shakeel and Tahira Saeed
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
Data Availability
References
6. Harmonizing the Cosmos: Radio Astronomy and AI Integration
Manjuleshwar Panda, Aadarsh Kumar Chaudhri and Mukesh Kumar Pandey
6.1 Introduction: The Synergy of Radio Astronomy and AI
6.1.1 Overview of Radio Astronomy and Its Significance in Modern Astrophysics
6.1.2 The Role of AI in Revolutionizing Data Analysis, Signal Processing, and Discovery
6.2 Foundations of Radio Astronomy: Unlocking the Invisible Universe
6.2.1 How Radio Telescopes Detect Celestial Signals
6.2.2 Key Discoveries in Radio Astronomy, from Pulsars to Fast Radio Bursts
6.2.3 The Challenges of Radio Data Acquisition and Processing
6.3 The Evolution of AI in Radio Astronomy
6.3.1 Early Computational Techniques in Radio Astronomy
6.3.2 Transition from Traditional Methods to AI-Driven Approaches
6.3.3 The Impact of Machine Learning, Deep Learning, and Neural Networks
6.4 AI-Powered Signal Processing: Detecting the Weakest Cosmic Signals
6.4.1 Noise Filtering and Denoising Techniques Using AI
6.4.2 Identifying and Classifying Radio Signals Using Machine Learning
6.4.3 AI’s Role in Mitigating Radio Frequency Interference
6.5 Fast Radio Bursts and AI: Solving One of Astronomy’s Biggest Mysteries
6.5.1 What are FRBs, and Why are They Important?
6.5.2 How AI is Improving Real-Time Detection and Classification
6.5.3 Case Studies of AI-Driven FRB Discoveries (e.g., CHIME/FRB)
6.6 AI in Pulsar and SETI Research: Searching for Cosmic Beacons
6.6.1 How AI is Advancing Pulsar Detection and Classification
6.6.2 The Role of AI in the Search for Extraterrestrial Intelligence
6.6.3 Future Prospects of AI-Assisted Extraterrestrial Communication Searches
6.7 AI in Very Long Baseline Interferometry and Image Reconstruction
6.7.1 The Importance of VLBI in High-Resolution Radio Imaging
6.7.2 AI’s Role in Improving Data Calibration and Synthesis Imaging
6.7.3 Case Studies (e.g., AI in the Event Horizon Telescope’s Black Hole Imaging)
6.8 AI and Large Radio Surveys: Managing the Data Tsunami
6.8.1 AI in Handling Massive Datasets from Radio Telescopes like SKA and LOFAR
6.8.2 Automated Classification of Celestial Objects Using AI
6.8.3 AI-Driven Anomaly Detection in Radio Sky Surveys
6.9 Future Prospects: AI and Next-Generation Radio Astronomy
6.9.1 The Integration of Quantum Computing and AI in Radio Astronomy
6.9.2 AI’s Role in the Square Kilometre Array and Beyond
6.9.3 Challenges and Ethical Considerations of AI in Astronomical Research
6.10 Conclusion: The Future of AI-Driven Radio Astronomy
Data Availability
References
Part II: Advanced Techniques, Observatories, and Future Prospects
7. Image Processing and Computer Vision in Astronomy
Deepak Pandey, Garima Punetha and Chetna Tewari
7.1 Introduction to Image Processing in Astronomy
7.1.1 Calibration
7.1.2 Alignment and Stacking
7.2 Applications of Image Processing in Astronomy
7.2.1 Gamma-Ray Bursts
7.2.2 Gravitational Wave Counterparts
7.2.3 Supernovae and Novae
7.2.4 Fast Radio Bursts
7.3 Processing Techniques for Detecting Transient Events
7.4 Specific Techniques for Detecting Key Transients
7.4.1 Gamma-Ray Bursts
7.4.2 Gravitational Wave Counterparts
7.4.3 Fast Radio Bursts
7.5 Role of Computer Vision in Astronomy
7.5.1 Object Detection and Classification
7.5.2 Transient Event Detection
7.5.3 Asteroid and Comet Tracking
7.6 Advantages of Using Computer Vision in Astronomy
7.6.1 Automated Object Detection and Classification
7.6.2 Noise Reduction and Image Enhancement
7.6.3 Motion Detection and Object Tracking
7.6.4 Super-Resolution and Image Reconstruction
7.7 Applications
7.7.1 Object Detection
7.8 Challenges in Astronomical Image
7.8.1 High Dimensionality
7.8.2 Storage and Computational Requirements
7.8.3 Real-Time Analysis
7.8.4 Interpretability
7.9 Challenges in Interpretability for Astronomy
7.9.1 Data High Dimensionality
7.10 Future Directions
7.11 Conclusion
Data Availability
Acknowledgement
References
8. Astroinformatics and Big Data Challenges
Kanthavel R., Adline Freeda R. and Dhaya R.
8.1 Introduction to Astroinformatics
8.2 Big Data in Astronomy
8.2.1 Sources of Astronomical Data
8.2.2 Volume, Velocity, and Variety Challenges
8.3 Data Management in Astroinformatics
8.3.1 Systems for Storing Data
8.3.2 Archiving and Retrieving Data
8.3.3 Preservation and Curation of Data
8.3.4 Data Archiving and Retrieval
8.3.5 Archiving Techniques
8.3.6 Metadata and Standardization
8.3.7 Data Retrieval in Astroinformatics
8.3.8 Astroinformatics in Practice: The Hubble Space Telescope Repository
8.4 Data Processing Techniques
8.4.1 Data Preprocessing and Cleaning
8.4.2 Data Reduction
8.4.3 Feature Extraction and Selection
8.5 Data Visualization in Astroinformatics
8.5.1 Principal Visualization Methods in Astroinformatics
8.5.2 Applications and Challenges of Visualization in Astroinformatics
8.5.3 Data Processing Techniques in Astroinformatics
8.6 Statistical Challenges in Astroinformatics
8.6.1 Rare Events and Data Limitations
8.6.2 Observational Constraints and Big Data Challenges
8.6.3 Addressing Statistical Challenges in Astroinformatics
8.7 Time-Domain Astronomy
8.7.1 Observing Transient Events in Astronomy
8.7.2 Time-Series Analysis in Astronomy
8.7.3 Future of Time-Domain Astronomy and Big Data
8.8 Future Directions in Astroinformatics and Big Data
8.8.1 AI and Automation in Astronomical Data Processing
8.8.2 Advancements in High-Performance and Quantum Computing
8.8.3 Collaborative Platforms and Open Science Initiatives
8.9 Conclusion
Data Availability
References
9. Autonomous Telescopes and Observatories
Himani Mehta, Shakti Singh, V.S. Pandey, Preeti Verma and Anagha Antony
9.1 Introduction
9.2 Historical Background of Telescopes
9.3 The Evolution of Telescopes
9.3.1 From Manual to Automated Systems
9.3.2 The Rise of Autonomous Observatories
9.4 Types of Telescopes and Their Uses
9.4.1 Refracting Telescopes
9.4.2 Reflecting Telescopes
9.4.3 Catadioptric Telescopes
9.4.3.1 Types of Catadioptric Telescopes
9.4.4 Radio Telescopes
9.4.4.1 Single-Dish Radio Telescopes
9.4.4.2 Radio Interferometry
9.4.5 X-Ray Telescopes
9.4.6 Ultraviolet Telescopes
9.5 The Role of AI in Autonomous Telescopes
9.5.1 Data Acquisition and Processing
9.5.2 Intelligent Scheduling and Decision-Making
9.5.3 Enhancing Image Quality
9.6 Detecting Techniques and Instruments
9.6.1 Evolution of Detectors
9.6.2 AI Role in Detectors
9.7 AI’s Role in Robotic Telescopes
9.7.1 Architectural Elements and Usability
9.7.2 AI-Powered Features
9.8 Challenges in Autonomous Astronomy
9.8.1 Ensuring Robustness and Reliability
9.8.2 Overcoming Navigation Challenges
9.8.3 Addressing Data Management and Security Issues
9.8.4 Ethical and Accountability Considerations
9.8.5 Integration and Maintenance Requirements
9.9 The Future of Autonomous Astronomy
9.9.1 Enhanced Observational Capabilities
9.9.2 Real-Time Data Processing and Discovery
9.9.3 Collaborative Networks of Autonomous Telescopes
9.9.4 Transforming the Role of Human Astronomers
9.9.5 Paving the Way for New Discoveries
9.10 Conclusion
Acknowledgement
References
10. Beyond Earth’s Horizon: AI’s Contribution to Space Exploration
Bhumika Sharma, Anil C. Mathur, Rama Sharma and Pratibha Antil
10.1 Introduction
10.2 The Evolution of AI in Space Exploration
10.2.1 Early Applications of AI in Space Missions
10.2.2 Milestones Like AI-Driven Navigation Systems and Planetary Rovers
10.3 Applications of AI in Modern Space Missions
10.3.1 Mars Rovers
10.3.2 Satellite Operations
10.3.3 Data Analysis
10.3.4 Mission Design and Planning
10.4 AI-Driven Space Robotics
10.4.1 Robotic Arms and Humanoid Robots Like Robonaut and CIMON
10.4.2 Autonomous Maintenance and Repair in Space
10.5 AI in Deep Space Missions and Exploration
10.5.1 Role of AI in Long-Term Missions to the Moon, Mars, and Beyond
10.5.2 Handling Communication Delays with Earth Through Autonomous Decision-Making Systems
10.5.3 AI’s Contribution to Understanding Extraterrestrial Environments
10.6 AI in Spacecraft Autonomy and Navigation
10.6.1 AI Algorithms in Discovering Exoplanets and Understanding the Cosmos
10.6.1.1 AI in Exoplanet Classification
10.6.1.2 AI in Atmospheric Analysis of Exoplanets
10.6.2 Predictive Models for Celestial Events
10.6.2.1 AI in Solar Flare Prediction
10.6.2.2 AI in Asteroid and Comet Trajectory Prediction
10.7 Challenges and Limitations of AI in Space Science
10.7.1 Reliability and Robustness of AI Systems in Extreme Conditions
10.7.2 Ethical Considerations and the Balance Between Human and AI Control
10.7.3 Data Privacy and Security in Space Communications
10.8 Future of AI in Space Exploration: Possibilities and Promises
10.8.1 AI’s Potential Role in Building Extraterrestrial Habitats
10.8.2 Integration with Quantum Computing for Enhanced Space Exploration
10.8.3 AI as a Partner in Interstellar Exploration and Understanding Alien Intelligence
10.9 Conclusion
Acknowledgment
References
11. Exploring Astrobiology and the Search for Extraterrestrial Intelligence (SETI)
Yamini Rani and Anurag Kasana
11.1 Introduction to Astrobiology and Search for Extraterrestrial Intelligence
11.1.1 The Key Components of Astrobiology
11.1.2 The Quest for Extraterrestrial Life
11.1.3 Future Prospects in Astrobiology
11.2 The Role of SETI in the Search for Extraterrestrial Intelligence
11.2.1 Exploring Astrobiology and SETI
11.2.2 Technological Advancement in SETI
11.2.3 Challenges and Future Prospects of SETI
11.3 The Origin of Astrobiology
11.3.1 Historical Context and Early Speculations
11.3.2 The Formation of Astrobiology as a Scientific Discipline
11.3.3 Key Milestones in the Development of Astrobiology
11.4 Understanding the Universe: A Foundation for Astrobiology
11.4.1 The Formation and Evolution of the Universe
11.4.2 The Conditions Necessary for Life
11.4.3 The Habitable Zone and Exoplanets
11.5 The Search for Life in the Solar System
11.5.1 Mars: The Red Planet and Its Potential for Life
11.5.2 The Surface and Atmosphere
11.5.3 Human Exploration and Colonization
11.5.4 Europa, Enceladus, and Other Icy Moons
11.5.5 Europa
11.5.6 Enceladus and Other Icy Moons
11.6 Venus and the Possibility of Aerial Biospheres
11.6.1 Astrobiological Mission and Technological Advances
11.6.2 Exoplanets and the Potential for Life Beyond the Solar System
11.6.3 Discovering Exoplanet Methods and Technologies
11.6.4 Transit Method
11.6.5 Radial Velocity Method
11.6.6 Direct Imaging
11.6.7 Gravitational Microlensing
11.6.8 Astrometry
11.7 The Role of Space Telescopes (Kepler, TESS, JWST)
11.7.1 Classification of Exoplanets: Super-Earths, Hot Jupiters, and More
11.7.2 Earth-Like Planets and Rogue Planets
11.7.3 Assessing Habitability: Atmospheres, Biosignatures, and Remote Sensing
11.8 The Search for Extraterrestrial Intelligence
11.8.1 The Drake Equation and Estimating the Probability of Extraterrestrial Civilization
11.8.2 Implications and Applications
11.8.3 Challenges and Criticisms
11.8.4 SETI Techniques and Technologies: Radio Signals, Optical SETI, and More
11.8.5 Emerging SETI Technologies and Techniques
11.8.6 Collaborative Efforts, Challenges, and Future Prospects in SETI
11.8.7 Major SETI Projects and Initiatives
11.9 The Fermi Paradox and the Great Silence
11.9.1 Core Aspects of the Fermi Paradox
11.9.2 Proposed Solutions to the Fermi Paradox
11.9.3 The Great Filter and the Fragility of Civilization
11.9.4 Anthropocentric Assumptions
11.10 Ethical and Philosophical Implications of Contacting Extraterrestrial Life
11.10.1 The Impact on Human Society and Civilization
11.10.2 Ethical Considerations: Messaging to Extraterrestrial Intelligence
11.10.3 The Potential for Interstellar Communication
11.10.4 Future Prospects in Astrobiology and SETI
11.11 Conclusion
Declaration Section
Data Availability
Acknowledgement
References
12. Anticipating the Unseen: AI’s Promise in Illuminating Astronomy’s Future
Ritika Joshi and Pratibha Fuloria
12.1 Introduction
12.1.1 Optimized Data Collection, Preprocessing, and Cutting-Edge Algorithms
12.1.2 Real-Time Intelligence and Simulation
12.1.3 Scientific Discovery
12.2 Modern Issues in Astronomy
12.2.1 The Exponential Growth of Data
12.2.2 Real-Time Data Processing and Complexity of Data Interpretation
12.2.3 Computational Limits and Data Archiving
12.2.4 Theoretical Gaps and Interpretative Challenges
12.3 AI’s Transformative Role in Astronomy
12.3.1 The Big Data Challenge in Astronomy
12.3.2 Revolutionizing Exoplanet Discovery
12.3.3 Advancing Astrophysics with AI: Monitoring, Simulation, and Prediction
12.3.4 Quantum AI and Ethical Considerations in Astronomy
12.4 Classification of Images and Its Application in Astronomy
12.4.1 Deep Learning in Astronomical Image Processing
12.4.2 Advanced Machine Learning for Astronomical Object Detection and Discovery
12.4.3 Neural Networks in Multispectral and Multimodal Imaging
12.5 Cosmological Simulations
12.5.1 AI-Driven Simulations of Cosmic Phenomena
12.5.2 Machine Learning Models for Predicting Large-Scale Cosmic Structures
12.5.3 AI Bridging Simulations and Observational Data
12.5.4 The Future: Integrated Real-Time Socio-Simulations and AI in Astrophysical Models
12.6 The Future: AI and Quantum Computing in Astronomy
12.6.1 Introduction to Quantum Computing
12.6.2 Enhancing AI’s Role in Astronomy with Quantum Computing
12.6.2.1 Quantum Machine Learning
12.6.2.2 Optimization and Efficiency
12.6.3 The Potential for More Complex Simulations
12.7 Challenges and the Path Forward
12.7.1 Algorithmic Bias in AI Models
12.7.2 Data Bias and Incomplete Datasets
12.7.3 Data Requirements and Challenges
12.7.4 Ethical Considerations in AI-Driven Astronomy
12.8 Strategies for Mitigating Challenges in AI-Driven Astronomy
12.8.1 Mitigating Algorithmic Bias
12.8.1.1 Diverse and Representative Datasets
12.8.1.2 Bias Audits and Fairness Metrics
12.8.2 Addressing Data Requirements and Challenges
12.8.2.1 Noise and Dimensionality Reduction
12.8.2.2 Semi-Supervised and Unsupervised Learning
12.8.2.3 Collaborative Data Sharing and Standardization
12.8.3 Ethical Considerations and AI Governance
12.8.3.1 Explainability and Transparency
12.8.3.2 Ethical AI Development Frameworks
12.8.3.3 Collaborative Governance and Accountability
12.8.3.4 AI Ethics Committees
12.8.4 Technical Solutions for Scaling Data Requirements
12.8.4.1 Distributed Computing and Cloud Infrastructure
12.8.4.2 Edge Computing and AI
12.9 Conclusion: Embracing the Future of Astronomical Discovery
Data Availability
Acknowledgement
References
Index
Back to Top
Foreword
Preface
Acknowledgement
Part I: Foundations and Core Applications of AI in Astronomy
1. Introduction to AI in Astronomy
Rahul Barnwal, Aman Kumar, Kala S. and Sree Ranjani Rajendran
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
2. Data Mining and Machine Learning in Astrophysics
Gissmol Saji and Sanjay Singh Bisht
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
Acknowledgement
References
3. The Role of Artificial Intelligence in the Discovery and Characterization of Exoplanets
Shraddha. Biswas, D. Bisht and Ing-Guey Jiang
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
Acknowledgement
References
4. Cosmology and Dark Matter Research
Arun Kumar Rathore, B. C. Chanyal and Sirley Marques-Bonham
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 ΛCDM Model
4.5.4 MOdified Newtonian Dynamics
4.5.4.1 Observational Evidence of MOND
4.6 ΛCDM and MOND
4.6.1 Interpretation of the ΛCDM–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
Data Availability
Acknowledgment
References
5. Gravitational Wave Detection
Muhammad Zeshan Ashraf, Farhat Shakeel and Tahira Saeed
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
Data Availability
References
6. Harmonizing the Cosmos: Radio Astronomy and AI Integration
Manjuleshwar Panda, Aadarsh Kumar Chaudhri and Mukesh Kumar Pandey
6.1 Introduction: The Synergy of Radio Astronomy and AI
6.1.1 Overview of Radio Astronomy and Its Significance in Modern Astrophysics
6.1.2 The Role of AI in Revolutionizing Data Analysis, Signal Processing, and Discovery
6.2 Foundations of Radio Astronomy: Unlocking the Invisible Universe
6.2.1 How Radio Telescopes Detect Celestial Signals
6.2.2 Key Discoveries in Radio Astronomy, from Pulsars to Fast Radio Bursts
6.2.3 The Challenges of Radio Data Acquisition and Processing
6.3 The Evolution of AI in Radio Astronomy
6.3.1 Early Computational Techniques in Radio Astronomy
6.3.2 Transition from Traditional Methods to AI-Driven Approaches
6.3.3 The Impact of Machine Learning, Deep Learning, and Neural Networks
6.4 AI-Powered Signal Processing: Detecting the Weakest Cosmic Signals
6.4.1 Noise Filtering and Denoising Techniques Using AI
6.4.2 Identifying and Classifying Radio Signals Using Machine Learning
6.4.3 AI’s Role in Mitigating Radio Frequency Interference
6.5 Fast Radio Bursts and AI: Solving One of Astronomy’s Biggest Mysteries
6.5.1 What are FRBs, and Why are They Important?
6.5.2 How AI is Improving Real-Time Detection and Classification
6.5.3 Case Studies of AI-Driven FRB Discoveries (e.g., CHIME/FRB)
6.6 AI in Pulsar and SETI Research: Searching for Cosmic Beacons
6.6.1 How AI is Advancing Pulsar Detection and Classification
6.6.2 The Role of AI in the Search for Extraterrestrial Intelligence
6.6.3 Future Prospects of AI-Assisted Extraterrestrial Communication Searches
6.7 AI in Very Long Baseline Interferometry and Image Reconstruction
6.7.1 The Importance of VLBI in High-Resolution Radio Imaging
6.7.2 AI’s Role in Improving Data Calibration and Synthesis Imaging
6.7.3 Case Studies (e.g., AI in the Event Horizon Telescope’s Black Hole Imaging)
6.8 AI and Large Radio Surveys: Managing the Data Tsunami
6.8.1 AI in Handling Massive Datasets from Radio Telescopes like SKA and LOFAR
6.8.2 Automated Classification of Celestial Objects Using AI
6.8.3 AI-Driven Anomaly Detection in Radio Sky Surveys
6.9 Future Prospects: AI and Next-Generation Radio Astronomy
6.9.1 The Integration of Quantum Computing and AI in Radio Astronomy
6.9.2 AI’s Role in the Square Kilometre Array and Beyond
6.9.3 Challenges and Ethical Considerations of AI in Astronomical Research
6.10 Conclusion: The Future of AI-Driven Radio Astronomy
Data Availability
References
Part II: Advanced Techniques, Observatories, and Future Prospects
7. Image Processing and Computer Vision in Astronomy
Deepak Pandey, Garima Punetha and Chetna Tewari
7.1 Introduction to Image Processing in Astronomy
7.1.1 Calibration
7.1.2 Alignment and Stacking
7.2 Applications of Image Processing in Astronomy
7.2.1 Gamma-Ray Bursts
7.2.2 Gravitational Wave Counterparts
7.2.3 Supernovae and Novae
7.2.4 Fast Radio Bursts
7.3 Processing Techniques for Detecting Transient Events
7.4 Specific Techniques for Detecting Key Transients
7.4.1 Gamma-Ray Bursts
7.4.2 Gravitational Wave Counterparts
7.4.3 Fast Radio Bursts
7.5 Role of Computer Vision in Astronomy
7.5.1 Object Detection and Classification
7.5.2 Transient Event Detection
7.5.3 Asteroid and Comet Tracking
7.6 Advantages of Using Computer Vision in Astronomy
7.6.1 Automated Object Detection and Classification
7.6.2 Noise Reduction and Image Enhancement
7.6.3 Motion Detection and Object Tracking
7.6.4 Super-Resolution and Image Reconstruction
7.7 Applications
7.7.1 Object Detection
7.8 Challenges in Astronomical Image
7.8.1 High Dimensionality
7.8.2 Storage and Computational Requirements
7.8.3 Real-Time Analysis
7.8.4 Interpretability
7.9 Challenges in Interpretability for Astronomy
7.9.1 Data High Dimensionality
7.10 Future Directions
7.11 Conclusion
Data Availability
Acknowledgement
References
8. Astroinformatics and Big Data Challenges
Kanthavel R., Adline Freeda R. and Dhaya R.
8.1 Introduction to Astroinformatics
8.2 Big Data in Astronomy
8.2.1 Sources of Astronomical Data
8.2.2 Volume, Velocity, and Variety Challenges
8.3 Data Management in Astroinformatics
8.3.1 Systems for Storing Data
8.3.2 Archiving and Retrieving Data
8.3.3 Preservation and Curation of Data
8.3.4 Data Archiving and Retrieval
8.3.5 Archiving Techniques
8.3.6 Metadata and Standardization
8.3.7 Data Retrieval in Astroinformatics
8.3.8 Astroinformatics in Practice: The Hubble Space Telescope Repository
8.4 Data Processing Techniques
8.4.1 Data Preprocessing and Cleaning
8.4.2 Data Reduction
8.4.3 Feature Extraction and Selection
8.5 Data Visualization in Astroinformatics
8.5.1 Principal Visualization Methods in Astroinformatics
8.5.2 Applications and Challenges of Visualization in Astroinformatics
8.5.3 Data Processing Techniques in Astroinformatics
8.6 Statistical Challenges in Astroinformatics
8.6.1 Rare Events and Data Limitations
8.6.2 Observational Constraints and Big Data Challenges
8.6.3 Addressing Statistical Challenges in Astroinformatics
8.7 Time-Domain Astronomy
8.7.1 Observing Transient Events in Astronomy
8.7.2 Time-Series Analysis in Astronomy
8.7.3 Future of Time-Domain Astronomy and Big Data
8.8 Future Directions in Astroinformatics and Big Data
8.8.1 AI and Automation in Astronomical Data Processing
8.8.2 Advancements in High-Performance and Quantum Computing
8.8.3 Collaborative Platforms and Open Science Initiatives
8.9 Conclusion
Data Availability
References
9. Autonomous Telescopes and Observatories
Himani Mehta, Shakti Singh, V.S. Pandey, Preeti Verma and Anagha Antony
9.1 Introduction
9.2 Historical Background of Telescopes
9.3 The Evolution of Telescopes
9.3.1 From Manual to Automated Systems
9.3.2 The Rise of Autonomous Observatories
9.4 Types of Telescopes and Their Uses
9.4.1 Refracting Telescopes
9.4.2 Reflecting Telescopes
9.4.3 Catadioptric Telescopes
9.4.3.1 Types of Catadioptric Telescopes
9.4.4 Radio Telescopes
9.4.4.1 Single-Dish Radio Telescopes
9.4.4.2 Radio Interferometry
9.4.5 X-Ray Telescopes
9.4.6 Ultraviolet Telescopes
9.5 The Role of AI in Autonomous Telescopes
9.5.1 Data Acquisition and Processing
9.5.2 Intelligent Scheduling and Decision-Making
9.5.3 Enhancing Image Quality
9.6 Detecting Techniques and Instruments
9.6.1 Evolution of Detectors
9.6.2 AI Role in Detectors
9.7 AI’s Role in Robotic Telescopes
9.7.1 Architectural Elements and Usability
9.7.2 AI-Powered Features
9.8 Challenges in Autonomous Astronomy
9.8.1 Ensuring Robustness and Reliability
9.8.2 Overcoming Navigation Challenges
9.8.3 Addressing Data Management and Security Issues
9.8.4 Ethical and Accountability Considerations
9.8.5 Integration and Maintenance Requirements
9.9 The Future of Autonomous Astronomy
9.9.1 Enhanced Observational Capabilities
9.9.2 Real-Time Data Processing and Discovery
9.9.3 Collaborative Networks of Autonomous Telescopes
9.9.4 Transforming the Role of Human Astronomers
9.9.5 Paving the Way for New Discoveries
9.10 Conclusion
Acknowledgement
References
10. Beyond Earth’s Horizon: AI’s Contribution to Space Exploration
Bhumika Sharma, Anil C. Mathur, Rama Sharma and Pratibha Antil
10.1 Introduction
10.2 The Evolution of AI in Space Exploration
10.2.1 Early Applications of AI in Space Missions
10.2.2 Milestones Like AI-Driven Navigation Systems and Planetary Rovers
10.3 Applications of AI in Modern Space Missions
10.3.1 Mars Rovers
10.3.2 Satellite Operations
10.3.3 Data Analysis
10.3.4 Mission Design and Planning
10.4 AI-Driven Space Robotics
10.4.1 Robotic Arms and Humanoid Robots Like Robonaut and CIMON
10.4.2 Autonomous Maintenance and Repair in Space
10.5 AI in Deep Space Missions and Exploration
10.5.1 Role of AI in Long-Term Missions to the Moon, Mars, and Beyond
10.5.2 Handling Communication Delays with Earth Through Autonomous Decision-Making Systems
10.5.3 AI’s Contribution to Understanding Extraterrestrial Environments
10.6 AI in Spacecraft Autonomy and Navigation
10.6.1 AI Algorithms in Discovering Exoplanets and Understanding the Cosmos
10.6.1.1 AI in Exoplanet Classification
10.6.1.2 AI in Atmospheric Analysis of Exoplanets
10.6.2 Predictive Models for Celestial Events
10.6.2.1 AI in Solar Flare Prediction
10.6.2.2 AI in Asteroid and Comet Trajectory Prediction
10.7 Challenges and Limitations of AI in Space Science
10.7.1 Reliability and Robustness of AI Systems in Extreme Conditions
10.7.2 Ethical Considerations and the Balance Between Human and AI Control
10.7.3 Data Privacy and Security in Space Communications
10.8 Future of AI in Space Exploration: Possibilities and Promises
10.8.1 AI’s Potential Role in Building Extraterrestrial Habitats
10.8.2 Integration with Quantum Computing for Enhanced Space Exploration
10.8.3 AI as a Partner in Interstellar Exploration and Understanding Alien Intelligence
10.9 Conclusion
Acknowledgment
References
11. Exploring Astrobiology and the Search for Extraterrestrial Intelligence (SETI)
Yamini Rani and Anurag Kasana
11.1 Introduction to Astrobiology and Search for Extraterrestrial Intelligence
11.1.1 The Key Components of Astrobiology
11.1.2 The Quest for Extraterrestrial Life
11.1.3 Future Prospects in Astrobiology
11.2 The Role of SETI in the Search for Extraterrestrial Intelligence
11.2.1 Exploring Astrobiology and SETI
11.2.2 Technological Advancement in SETI
11.2.3 Challenges and Future Prospects of SETI
11.3 The Origin of Astrobiology
11.3.1 Historical Context and Early Speculations
11.3.2 The Formation of Astrobiology as a Scientific Discipline
11.3.3 Key Milestones in the Development of Astrobiology
11.4 Understanding the Universe: A Foundation for Astrobiology
11.4.1 The Formation and Evolution of the Universe
11.4.2 The Conditions Necessary for Life
11.4.3 The Habitable Zone and Exoplanets
11.5 The Search for Life in the Solar System
11.5.1 Mars: The Red Planet and Its Potential for Life
11.5.2 The Surface and Atmosphere
11.5.3 Human Exploration and Colonization
11.5.4 Europa, Enceladus, and Other Icy Moons
11.5.5 Europa
11.5.6 Enceladus and Other Icy Moons
11.6 Venus and the Possibility of Aerial Biospheres
11.6.1 Astrobiological Mission and Technological Advances
11.6.2 Exoplanets and the Potential for Life Beyond the Solar System
11.6.3 Discovering Exoplanet Methods and Technologies
11.6.4 Transit Method
11.6.5 Radial Velocity Method
11.6.6 Direct Imaging
11.6.7 Gravitational Microlensing
11.6.8 Astrometry
11.7 The Role of Space Telescopes (Kepler, TESS, JWST)
11.7.1 Classification of Exoplanets: Super-Earths, Hot Jupiters, and More
11.7.2 Earth-Like Planets and Rogue Planets
11.7.3 Assessing Habitability: Atmospheres, Biosignatures, and Remote Sensing
11.8 The Search for Extraterrestrial Intelligence
11.8.1 The Drake Equation and Estimating the Probability of Extraterrestrial Civilization
11.8.2 Implications and Applications
11.8.3 Challenges and Criticisms
11.8.4 SETI Techniques and Technologies: Radio Signals, Optical SETI, and More
11.8.5 Emerging SETI Technologies and Techniques
11.8.6 Collaborative Efforts, Challenges, and Future Prospects in SETI
11.8.7 Major SETI Projects and Initiatives
11.9 The Fermi Paradox and the Great Silence
11.9.1 Core Aspects of the Fermi Paradox
11.9.2 Proposed Solutions to the Fermi Paradox
11.9.3 The Great Filter and the Fragility of Civilization
11.9.4 Anthropocentric Assumptions
11.10 Ethical and Philosophical Implications of Contacting Extraterrestrial Life
11.10.1 The Impact on Human Society and Civilization
11.10.2 Ethical Considerations: Messaging to Extraterrestrial Intelligence
11.10.3 The Potential for Interstellar Communication
11.10.4 Future Prospects in Astrobiology and SETI
11.11 Conclusion
Declaration Section
Data Availability
Acknowledgement
References
12. Anticipating the Unseen: AI’s Promise in Illuminating Astronomy’s Future
Ritika Joshi and Pratibha Fuloria
12.1 Introduction
12.1.1 Optimized Data Collection, Preprocessing, and Cutting-Edge Algorithms
12.1.2 Real-Time Intelligence and Simulation
12.1.3 Scientific Discovery
12.2 Modern Issues in Astronomy
12.2.1 The Exponential Growth of Data
12.2.2 Real-Time Data Processing and Complexity of Data Interpretation
12.2.3 Computational Limits and Data Archiving
12.2.4 Theoretical Gaps and Interpretative Challenges
12.3 AI’s Transformative Role in Astronomy
12.3.1 The Big Data Challenge in Astronomy
12.3.2 Revolutionizing Exoplanet Discovery
12.3.3 Advancing Astrophysics with AI: Monitoring, Simulation, and Prediction
12.3.4 Quantum AI and Ethical Considerations in Astronomy
12.4 Classification of Images and Its Application in Astronomy
12.4.1 Deep Learning in Astronomical Image Processing
12.4.2 Advanced Machine Learning for Astronomical Object Detection and Discovery
12.4.3 Neural Networks in Multispectral and Multimodal Imaging
12.5 Cosmological Simulations
12.5.1 AI-Driven Simulations of Cosmic Phenomena
12.5.2 Machine Learning Models for Predicting Large-Scale Cosmic Structures
12.5.3 AI Bridging Simulations and Observational Data
12.5.4 The Future: Integrated Real-Time Socio-Simulations and AI in Astrophysical Models
12.6 The Future: AI and Quantum Computing in Astronomy
12.6.1 Introduction to Quantum Computing
12.6.2 Enhancing AI’s Role in Astronomy with Quantum Computing
12.6.2.1 Quantum Machine Learning
12.6.2.2 Optimization and Efficiency
12.6.3 The Potential for More Complex Simulations
12.7 Challenges and the Path Forward
12.7.1 Algorithmic Bias in AI Models
12.7.2 Data Bias and Incomplete Datasets
12.7.3 Data Requirements and Challenges
12.7.4 Ethical Considerations in AI-Driven Astronomy
12.8 Strategies for Mitigating Challenges in AI-Driven Astronomy
12.8.1 Mitigating Algorithmic Bias
12.8.1.1 Diverse and Representative Datasets
12.8.1.2 Bias Audits and Fairness Metrics
12.8.2 Addressing Data Requirements and Challenges
12.8.2.1 Noise and Dimensionality Reduction
12.8.2.2 Semi-Supervised and Unsupervised Learning
12.8.2.3 Collaborative Data Sharing and Standardization
12.8.3 Ethical Considerations and AI Governance
12.8.3.1 Explainability and Transparency
12.8.3.2 Ethical AI Development Frameworks
12.8.3.3 Collaborative Governance and Accountability
12.8.3.4 AI Ethics Committees
12.8.4 Technical Solutions for Scaling Data Requirements
12.8.4.1 Distributed Computing and Cloud Infrastructure
12.8.4.2 Edge Computing and AI
12.9 Conclusion: Embracing the Future of Astronomical Discovery
Data Availability
Acknowledgement
References
Index
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