25 citations · 68 across the 11 of their papers we have counts for
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astro-ph.CO2022★ 20 cited
The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps
A. Ferragamo, D. de Andres, A. Sbriglio +9
We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate…
astro-ph.CO2022★ 25 cited
Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
Daniel de Andres, Gustavo Yepes, Federico Sembolini +5
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 (st…