LEADER 05915nam 22006735 450 001 9910751395303321 005 20231014213211.0 010 $a3-031-34167-8 024 7 $a10.1007/978-3-031-34167-0 035 $a(MiAaPQ)EBC30787880 035 $a(Au-PeEL)EBL30787880 035 $a(DE-He213)978-3-031-34167-0 035 $a(PPN)272914703 035 $a(CKB)28505217000041 035 $a(EXLCZ)9928505217000041 100 $a20231014d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Astrophysics $eProceedings of the ML4Astro International Conference 30 May - 1 Jun 2022 /$fedited by Filomena Bufano, Simone Riggi, Eva Sciacca, Francesco Schilliro 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (206 pages) 225 1 $aAstrophysics and Space Science Proceedings,$x1570-6605 ;$v60 311 08$aPrint version: Bufano, Filomena Machine Learning for Astrophysics Cham : Springer International Publishing AG,c2023 9783031341663 327 $aMachine Learning for H? Emitters Classification -- Stellar Dating Using Chemical Clocks and Bayesian Inference -- Detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vector Machine -- Dust Extinction from Random Forest Regression of Interstellar Lines -- QSOs Selection in Highly Unbalanced Photometric Datasets: The "Michelangelo" Reverse-Selection Method -- Radio Galaxy Detection Prediction with Ensemble Machine Learning -- A Machine Learning Suite to Halo-Galaxy Connection -- New Applications of Graph Neural Networks in Cosmology -- Detection of Point Sources in Maps of the Temperature Anisotropies of the Cosmic Microwave Background -- Reconstruction and Particle Identification with CYGNO Experiment -- Event Reconstruction for Neutrino Telescopes -- Classification of Evolved Stars with (Unsupervised) Machine Learning Post Proceedings -- Patterns in the Chaos: An Unsupervised View of Galactic Supernova Remnants -- Clustering of Galaxy Spectra: An Unsupervised Approach with Fisher-EM -- Unsupervised Classification Reveals New Evolutionary Pathways -- In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe -- Classifying Gamma-Ray Burst X-Ray Afterglows with a Variational Autoencoder -- Reconstructing Blended Galaxies with Machine Learning -- Time Domain Astroinformatics -- A Convolutional Neural Network to Characterise the Internal Structure of Stars -- Finding Stellar Flares with Recurrent Deep Neural Networks -- Planetary Markers in Stellar Spectra: Jupiter-Host Star Classification -- Using Convolutional Neural Networks to Detect and Confirm Exoplanets -- Machine Learning Applied to X-Ray Spectra: Separating Stars from Active Galactic Nuclei -- Classification of System Variability Using A CNN -- Deep Learning Processing and Analysis of Mock Astrophysical Observations -- Deep Neural Networks for Source Detection in Radio Astronomical Maps -- Radio Image Segmentation with Autoencoders -- Citizen Science and Machine Learning: Towards a Robust Large-Scale Automatic Classification in Astronomy -- Background Estimation in Fermi Gamma-Ray Burst Monitor Lightcurves Through a Neural Network -- Machine Learning Investigations for LSST: Strong Lens Mass Modeling and Photometric Redshift Estimation -- Multi-Band Photometry and Photometric Redshifts from Astronomical Images -- Inference of Galaxy Clusters Mass Radial Profiles from Compton-? Maps with Deep Learning Technique -- Deep Learning 21cm Lightcones in 3D -- ConvNets for Enhanced Background Discrimination in the Diffuse Supernova Neutrino-Background (DSNB) Search -- Deep Neural Networks for Single-Line Event Direction Reconstruction in ANTARES -- Cats Vs Dogs, Photons Vs Hadrons -- Events Classification in MAGIC Through Convolutional Neural Network Trained with Images of Observed Gamma-Ray Events -- Federated Learning Meets HPC and Cloud -- Integration and Deployment of Model Serving Framework at Production Scale -- Predictive Maintenance for Array of Cherenkov Telescopes. 330 $aThis book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics. 410 0$aAstrophysics and Space Science Proceedings,$x1570-6605 ;$v60 606 $aAstrophysics 606 $aMachine learning 606 $aArtificial intelligence 606 $aAstronomy$xObservations 606 $aAstrophysics 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aAstronomy, Observations and Techniques 615 0$aAstrophysics. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aAstronomy$xObservations. 615 14$aAstrophysics. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aAstronomy, Observations and Techniques. 676 $a523.010285631 676 $a523.010285631 700 $aBufano$b Filomena$01432922 701 $aRiggi$b Simone$01432923 701 $aSciacca$b Eva$01432924 701 $aSchilliro$b Francesco$0430037 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910751395303321 996 $aMachine Learning for Astrophysics$93577991 997 $aUNINA