49 citations · 204 across the 10 of their papers we have counts for
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astro-ph.CO2022★ 35 cited
A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton parameter maps
Daniel de Andres, Weiguang Cui, Florian Ruppin +8
Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological para…
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…