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Record Nr. |
UNINA9910751395303321 |
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Autore |
Bufano Filomena |
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Titolo |
Machine Learning for Astrophysics : Proceedings of the ML4Astro International Conference 30 May - 1 Jun 2022 / / edited by Filomena Bufano, Simone Riggi, Eva Sciacca, Francesco Schilliro |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (206 pages) |
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Collana |
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Astrophysics and Space Science Proceedings, , 1570-6605 ; ; 60 |
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Altri autori (Persone) |
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RiggiSimone |
SciaccaEva |
SchilliroFrancesco |
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Disciplina |
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Soggetti |
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Astrophysics |
Machine learning |
Artificial intelligence |
Astronomy - Observations |
Machine Learning |
Artificial Intelligence |
Astronomy, Observations and Techniques |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Machine 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 |
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