Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster's projected total mass distribution from mock observational data, i.e. stars

Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions

In this paper we present the European Low Frequency Survey (ELFS), a project that will enable the detection of primordial B-mode polarization by measuring the Galactic and extra-Galactic foregrounds in the 5- 120 GHz frequency window. Indeed, the main difficulty in measuring the Bmode polarization comes not just from its sheer faintness, but from