Machine Learning methods to estimate observational properties of galaxy clusters in large volume cos...
Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only cluster-size halos. The training set is built from THE THREE HUNDRED project which consists of a series of zoomed...
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Full title
Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
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TN_cdi_proquest_journals_2654700110
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2654700110
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2204.10751
