The eROSITA Final Equatorial-Depth Survey (eFEDS): A Machine Learning Approach to Infer Galaxy Cluster Masses from eROSITA X-ray Images

2023/04/28 by Sven Krippendorf, Nicolás Barón Pérez, Krippendorf, Sven +32 · 1 citation
Mathematics · Physics and Astronomy · #Astronomy and Astrophysical Research #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Statistical and numerical algorithms

paper · pdf · doi:10.48550/arxiv.2305.00016

openalex publication_date 2023/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We develop a neural network based pipeline to estimate masses of galaxy clusters with a known redshift directly from photon information in X-rays. Our neural networks are trained using supervised learning on simulations of eROSITA observations, focusing in this paper on the Final Equatorial Depth Survey (eFEDS). We use convolutional neural networks which are modified to include additional information of the cluster, in particular its redshift. In contrast to existing work, we utilize simulations including background and point sources to develop a tool which is usable directly on observational eROSITA data for an extended mass range from group size halos to massive clusters with masses in between 10¹³M_\odot

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