2k citations · 28.3k across the 241 of their papers we have counts for
205 papers · 1 filter
The star-forming past and quenching of brightest cluster galaxies in TNG-Cluster
Sunna Gottschewski, Joey Braspenning, Annalisa Pillepich +2
The Brightest Cluster Galaxies (BCGs) in the most massive galaxy clusters of our Universe are, at present, mostly quiescent, elliptical, and red. However, their formation histories…
Inferring magnetic field strengths in high-redshift lensing galaxies from Faraday rotation measure observations
Stefan Reissl, Ralf S. Klessen, Annalisa Pillepich +3
Observations of Faraday rotation in the lensing galaxy of CLASS B1152+199 at z = 0.439 suggest that efficient dynamo mechanisms had already amplified galactic magnetic fields by th…
Exploring the AGN population in protoclusters: results from the TNG300 simulation and comparison with observations
A. Traina, F. Vito, A. Pillepich +7
In this work, we investigate the physical properties and cosmic evolution of galaxies and AGN in a sample of 280 protoclusters identified in the TNG300 simulation, selected to end…
The host halo masses of AGNs and quasars at with TNG-Cluster, FLAMINGO and other cosmological galaxy simulations
Akanksha Kapahtia, Annalisa Pillepich, Joey Braspenning +5
Most observations and clustering analyses suggest that quasars inhabit a narrow range of dark-matter halo masses ( M) across cosmic time (). Recen…
Bulk vs. turbulent motions at the centres of galaxy clusters: AGN-driven turbulence according to TNG-Cluster
Bipradeep Saha, Annalisa Pillepich, Joey Braspenning +3
The highly dynamic intracluster medium (ICM) influences cluster thermodynamic evolution and probes key physical processes. Quantifying the non-thermal motions is therefore essentia…
ERGO-ML: The assembly histories of HSC galaxy images via invertible neural networks, contrastive learning, and cosmological simulations
Lukas Eisert, Connor Bottrell, Annalisa Pillepich +4
In this paper of ERGO-ML (Extracting Reality from Galaxy Observables with Machine Learning), we develop a model that infers the merger/assembly histories of galaxies directly from…