35 citations · 64 across the 3 of their papers we have counts for
3 papers
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…
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…
Mass Estimation of Planck Galaxy Clusters using Deep Learning
Daniel de Andres, Weiguang Cui, Florian Ruppin +6
Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some…