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001482612 001__ 1482612
001482612 003__ OCoLC
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001482612 019__ $$a1404288015$$a1404432895$$a1404445268
001482612 020__ $$a9783031341670$$q(electronic bk.)
001482612 020__ $$a3031341678$$q(electronic bk.)
001482612 020__ $$z9783031341663
001482612 020__ $$z303134166X
001482612 0247_ $$a10.1007/978-3-031-34167-0$$2doi
001482612 035__ $$aSP(OCoLC)1405851028
001482612 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dOCLKB$$dYDX$$dEBLCP$$dYDX$$dOCLCO$$dOCLCF
001482612 049__ $$aISEA
001482612 050_4 $$aQB51.3.E43$$bI58 2022
001482612 08204 $$a523.010285631$$223/eng/20231024
001482612 1112_ $$aInternational Conference on Machine Learning for Astrophysics$$n(1st :$$d2022 :$$cCatania, Italy ; Online)
001482612 24510 $$aMachine learning for astrophysics :$$bproceedings of the ML4Astro International Conference 30 May-1 June 2022 /$$cFilomena Bufano, Simone Riggi, Eva Sciacca, Francesco Schilliro, editors.
001482612 2463_ $$aML4Astro 2022
001482612 264_1 $$aCham :$$bSpringer,$$c2023.
001482612 300__ $$a1 online resource (180 pages) :$$billustrations (black and white, and color).
001482612 336__ $$atext$$btxt$$2rdacontent
001482612 337__ $$acomputer$$bc$$2rdamedia
001482612 338__ $$aonline resource$$bcr$$2rdacarrier
001482612 4901_ $$aAstrophysics and space science proceedings ;$$vvolume 60
001482612 500__ $$aIncludes index.
001482612 5050_ $$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.
001482612 506__ $$aAccess limited to authorized users.
001482612 520__ $$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.
001482612 588__ $$aDescription based on print version record.
001482612 650_6 $$aAstrophysique$$xInformatique$$vCongrès.
001482612 650_6 $$aApprentissage automatique$$vCongrès.
001482612 650_0 $$aAstrophysics$$xData processing$$vCongresses.
001482612 650_0 $$aMachine learning$$vCongresses.$$vCongresses$$0(DLC)sh2008107143
001482612 655_0 $$aElectronic books.
001482612 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001482612 7001_ $$aBufano, Filomena,$$eeditor.
001482612 7001_ $$aRiggi, Simone,$$eeditor.
001482612 7001_ $$aSciacca, Eva,$$eeditor.
001482612 7001_ $$aSchilliro, Francesco,$$eeditor.
001482612 77608 $$iPrint version:$$aML4Astro International Conference (2022), creator.$$tMachine learning for astrophysics.$$dCham : Springer, 2023$$z9783031341663$$w(OCoLC)1388649582
001482612 830_0 $$aAstrophysics and space science proceedings ;$$vv. 60.
001482612 852__ $$bebk
001482612 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-34167-0$$zOnline Access$$91397441.1
001482612 909CO $$ooai:library.usi.edu:1482612$$pGLOBAL_SET
001482612 980__ $$aBIB
001482612 980__ $$aEBOOK
001482612 982__ $$aEbook
001482612 983__ $$aOnline
001482612 994__ $$a92$$bISE