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[null,"1.","Record Nr.",null,"UNINA9911031642003321"]
[null,null,"Autore",null,"Chandra Yogesh"]
[null,null,"Titolo",null,"The Intelligent Universe : AI's Role in Astronomy"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"1-394-35551-3"]
[{"$nulls":4},"1-394-35550-5"]
[{"$nulls":4},"1-394-35549-1"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (516 pages)"]
[null,null,"Disciplina",null,"522/.8563"]
[null,null,"Soggetti",null,"Astronomy - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"1.9.1 AI-Enhanced Gravitational Wave Detection -- 1.9.2 ML for Anomaly Detection in Astronomical Data -- 1.9.3 AI-Driven Meteor Shower Mapping -- 1.10 Challenges and Limitations of AI in Astronomy -- 1.10.1 Sources of Bias in Astronomical Data -- 1.10.2 Mitigating Bias in AI Applications -- 1.10.3 The Importance of Interpretability: Understanding How AI Models Make Decisions -- 1.10.4 Key Approaches to Interpretability -- 1.11 The Future of AI in Astronomy: Opportunities and Horizons -- 1.12 Conclusion -- References -- Chapter 2 Data Mining and Machine Learning in Astrophysics -- 2.1 Introduction -- 2.2 Foundations of Data Mining and Machine Learning -- 2.2.1 Data Science and Its Components -- 2.2.2 Classical Machine Learning Versus Deep Learning -- 2.2.3 Data Mining Software and Tools -- 2.3 Machine Learning Applications in Astrophysics -- 2.3.1 Supervised Learning Techniques -- 2.3.2 Unsupervised Learning Techniques -- 2.3.3 Structure of Training Data: Supervised and Unsupervised -- 2.3.4 Semi-Supervised Learning Techniques -- 2.4 Role of Machine Learning in Key Astrophysical Research Areas -- 2.4.1 Exoplanet Detection -- 2.4.2 Gravitational Wave Analysis -- 2.4.3 Galaxy Classification -- 2.4.4 Transient Event Identification -- 2.5 Challenges in the Era of Big Data -- 2.5.1 Managing Vast Data Volumes -- 2.5.2 Addressing Observational Noise and Data Imbalance -- 2.5.3 Enhancing Model Interpretability -- 2.6 Bridging Observations and Theory -- 2.6.1 Enhancing Theoretical Simulations with Observational Data -- 2.6.2 Revolutionizing Astrophysical Simulations -- 2.6.3 Processing and Analyzing Vast Astrophysical Datasets -- 2.6.4 Discovering New Astrophysical Phenomena -- 2.7 The Future: Autonomous Observatories and Predictive Models -- 2.7.1 Real-Time Event Detection and Response -- 2.7.2 Predictive Modeling in Astrophysics."]
[{"$nulls":4},"2.7.3 A New Era of Astrophysical Discovery -- 2.8 Conclusion -- Data Availability -- Acknowledgement -- References -- 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 -- "]
[{"$nulls":4},"3.7.1.5 Optimization of Surveys -- 3.7.1.6 Collaborative Efforts with Simulations -- 3.7.2 Role of AI-Based Tools in Finding Habitable Planets."]
[{"$nulls":4},"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 -- 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."]
[{"$nulls":4},"4.10.7 Accelerating Discoveries with AI-Driven Hypotheses -- 4.11 AI's Role in Quantum Simulations of Dark Matter -- 4.12 Challenges and Future Prospects -- 4.12.1 Toward a Dark Matter Breakthrough -- 4.12.2 Handling Vast and Complex Datasets -- 4.13 Enhancing Analysis and Interpretation of Astronomical Data -- 4.13.1 Advanced Cosmological Simulations -- 4.13.2 Direct and Indirect Detection Support -- 4.14 AI in Theory Development and Hypothesis Generation -- 4.15 Challenges and Future Prospects -- 4.16 Conclusion -- Data Availability -- Acknowledgment -- References -- Chapter 5 Gravitational Wave Detection -- 5.1 Introduction -- 5.1.1 Theoretical Foundations of Gravitational Waves -- 5.1.2 Early Indirect Evidence and Predictions -- 5.1.3 The Breakthrough of Direct Detection -- 5.2 Gravitational Wave Observatories and Detection Techniques -- 5.2.1 LIGO, Virgo, and KAGRA: Current Ground-Based Detectors -- 5.2.2 Space-Based Observatories: LISA and Beyond -- 5.2.3 Sensitivity and Noise Reduction Strategies -- 5.3 Multi-Messenger Astronomy and Astrophysical Sources -- 5.3.1 Neutron Star Mergers and Kilonovae -- 5.3.2 Black Hole Mergers and Event Horizon Studies -- 5.3.3 Exotic Sources and High-Energy Astrophysics -- 5.4 Artificial Intelligence in Gravitational Wave Detection -- 5.4.1 Machine Learning for Signal Processing -- 5.4.2 Deep Learning for Event Classification -- 5.4.3 AI-Driven Noise Filtering and Optimization -- 5.5 Challenges and Future Prospects -- 5.5.1 Fundamental Sensitivity Limits of Current Detectors -- 5.5.2 Next-Generation Observatories: Einstein Telescope and Cosmic Explorer -- 5.5.3 AI's Expanding Role in Future Gravitational Wave Research -- 5.6 Conclusion -- Data Availability -- References -- Chapter 6 Harmonizing the Cosmos: Radio Astronomy and AI "]
[{"$nulls":4},"Integration -- 6.1 Introduction: The Synergy of Radio Astronomy and AI."]
[{"$nulls":4},"6.1.1 Overview of Radio Astronomy and Its Significance in Modern Astrophysics."]
[null,null,null,"Sommario/riassunto","Uncover the universe's secrets with this essential guide that provides a comprehensive exploration of how artificial intelligence is revolutionizing modern astronomical research."]