Titlebook: Machine Learning for Astrophysics; Proceedings of the M Filomena Bufano,Simone Riggi,Francesco Schilliro Conference proceedings 2023 The Ed

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Time Domain Astroinformatics, crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. Therefore, a change of perspective is required to explore Time Domain Astronomy (TDA) in an efficient and semi-automatic way, introducing the Time Domain Astroinformatics as the virtuous synergy between TDA and Data Science domains.

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Conference proceedings 2023and 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..

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Detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vecr the accuracy of QPO detection using machine learning methods is higher than the significance of confidence levels obtained by the use of Timmer and Koenig’s (Astron. Astrophys. 300:707–710, 1995) simulations.

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1570-6591 . The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics..978-3-031-34169-4978-3-031-34167-0Series ISSN 1570-6591 Series E-ISSN 1570-6605

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Patterns in the Chaos: An Unsupervised View of Galactic Supernova Remnants,o continuum imagery; and (ii) a clustering stage that groups together objects that display similar features in the resulting latent space. By applying this pipeline to a representative sample of Galactic SNRs, we found several physically meaningful subsets sharing common observational features.

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Unsupervised Classification Reveals New Evolutionary Pathways,xies . smaller than other red galaxies of a similar stellar mass, revealing the first sample of red nuggets at intermediate redshift. On the other end, a class of blue dwarf galaxies is composed mainly of AGN, challenging commonly used mid-infrared AGN selections.