Machine Learning for Astrophysics

Table of Contents

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   Book Overview
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   Chapter 1 Machine Learning for H α $$\alpha $$ Emitters Classification
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   Chapter 2 Stellar Dating Using Chemical Clocks and Bayesian Inference
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   Chapter 3 Detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vector Machine
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   Chapter 4 Dust Extinction from RF Regression of Interstellar Lines
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   Chapter 5 QSOs Selection in Highly Unbalanced Photometric Datasets: The “Michelangelo” Reverse-Selection Method
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   Chapter 6 Radio Galaxy Detection Prediction with Ensemble Machine Learning
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   Chapter 7 A Machine Learning Suite to Halo-Galaxy Connection
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   Chapter 8 New Applications of Graph Neural Networks in Cosmology
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    Chapter 9 Detection of Point Sources in Maps of the Temperature Anisotropies of the Cosmic Microwave Background
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    Chapter 10 Reconstruction and Particle Identification with CYGNO Experiment
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    Chapter 11 Event Reconstruction for Neutrino Telescopes
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    Chapter 12 Classification of Evolved Stars with (Unsupervised) Machine Learning
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    Chapter 13 Patterns in the Chaos: An Unsupervised View of Galactic Supernova Remnants
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    Chapter 14 Clustering of Galaxy Spectra: An Unsupervised Approach with Fisher-EM
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    Chapter 15 Unsupervised Classification Reveals New Evolutionary Pathways
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    Chapter 16 In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe
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    Chapter 17 Classifying Gamma-Ray Burst X-Ray Afterglows with a Variational Autoencoder
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    Chapter 18 Reconstructing Blended Galaxies with Machine Learning
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    Chapter 19 Time Domain Astroinformatics
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    Chapter 20 A Convolutional Neural Network to Characterise the Internal Structure of Stars
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    Chapter 21 Finding Stellar Flares with Recurrent Deep Neural Networks
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    Chapter 22 Planetary Markers in Stellar Spectra: Jupiter-Host Star Classification
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    Chapter 23 Using Convolutional Neural Networks to Detect and Confirm Exoplanets
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    Chapter 24 Machine Learning Applied to X-Ray Spectra: Separating Stars from Active Galactic Nuclei
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    Chapter 25 Classification of System Variability Using a CNN
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    Chapter 26 Deep Learning Processing and Analysis of Mock Astrophysical Observations
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    Chapter 27 Deep Neural Networks for Source Detection in Radio Astronomical Maps
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    Chapter 28 Radio Image Segmentation with Autoencoders
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    Chapter 29 Citizen Science and Machine Learning: Towards a Robust Large-Scale Automatic Classification in Astronomy
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    Chapter 30 Background Estimation in Fermi Gamma-Ray Burst Monitor Lightcurves Through a Neural Network
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    Chapter 31 Machine Learning Investigations for LSST: Strong Lens Mass Modeling and Photometric Redshift Estimation
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    Chapter 32 Multi-Band Photometry and Photometric Redshifts from Astronomical Images
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    Chapter 33 Inference of Galaxy Clusters’ Mass Radial Profiles from Compton- y Maps with Deep Learning Techniques
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    Chapter 34 Deep Learning 21 cm Lightcones in 3D
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    Chapter 35 ConvNets for Enhanced Background Discrimination in the Diffuse Supernova Neutrino Background Search
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    Chapter 36 Deep Neural Networks for Single-Line Event Direction Reconstruction in ANTARES
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    Chapter 37 Cats vs Dogs, Photons vs Hadrons
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    Chapter 38 Events Classification in MAGIC Through Convolutional Neural Network Trained with Images of Observed Gamma-Ray Events
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    Chapter 39 Federated Learning Meets HPC and Cloud
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    Chapter 40 Integration and Deployment of Model Serving Framework at Production Scale
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    Chapter 41 Predictive Maintenance for Array of Cherenkov Telescopes