371 citations · 1k across the 37 of their papers we have counts for
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astro-ph.GA2023
O Corona, where art thou? eROSITA's view of UV-optical-IR variability-selected massive black holes in low-mass galaxies
R. Arcodia, A. Merloni, J. Comparat +16
Finding massive black holes (MBHs, ) in the nuclei of low-mass galaxies () is crucial to constrain seeding and…
astro-ph.CO2023★ 4 cited
The eROSITA Final Equatorial-Depth Survey (eFEDS): A Machine Learning Approach to Infer Galaxy Cluster Masses from eROSITA X-ray Images
Sven Krippendorf, Nicolas Baron Perez, Esra Bulbul +15
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 u…