526 citations · 2k across the 43 of their papers we have counts for
7 papers · 1 filter
Photometric redshifts for active galactic nuclei with LePHARE for the Vera C. Rubin Observatory
R. Shirley, M. Salvato, J. Cohen-Tanugi +31
Active Galactic Nuclei (AGN) play a crucial role in galaxy evolution, but they are a minority of extragalactic sources with diverse Spectral Energy Distributions (SEDs), which depe…
The incidence of eROSITA X-ray AGN in the local Universe: from dwarf to massive galaxies
Z. Igo, A. Merloni, A. Georgakakis +22
Combining deep, wide-area X-ray surveys with multi-wavelength catalogues provides insights into rare, highly-accreting AGN and low-mass galaxies at low redshift, the latter potenti…
The SRG/eROSITA All-Sky Survey DR2: Cumulative X-ray catalogues from the first three surveys and multi-wavelength counterparts in the western Galactic hemisphere
M. E. Ramos-Ceja, G. Lamer, M. Salvato +111
The eROSITA telescope array on board the Spektrum-Roentgen-Gamma (SRG) mission began its all-sky survey program in December 2019, scanning the sky at an approximately six-month cad…
S-PLUS Clusters And Large-scale Environments (SCALE): II. PZWav versus redMaPPer identification of eRosita groups
L. Doubrawa, A. Finoguenov, E. S. Cypriano +10
We present the construction and characterization of a multi-wavelength catalog of galaxy groups and clusters by matching optical detections from the Southern Photometric Local Univ…
Substructure in redMaPPer clusters and its impact on X-ray morphology and scaling relations
R. Tuomainen, A. Finoguenov, J. Comparat +1
We statistically quantified the prevalence and properties of substructure in optical galaxy clusters and directly investigated its impact on X-ray morphology and scaling relations,…
Cosmology with galaxy clusters using machine learning. Application to eROSITA Data
Fucheng Zhong, Nicola R. Napolitano, Johan Comparat +8
Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Fo…