From the 1 of 10 linked papers with an AI index.
10 papers
Interpreting the stacked kinetic SZ effect I: velocity reconstruction and non-linear velocity effects
Lurdes Ondaro-Mallea, Raul E. Angulo, Boryana Hadzhiyska +1
The stacked kinetic Sunyaev-Zel'dovich (kSZ) signal probes the velocity-weighted projected gas momentum around galaxies, and is emerging as a powerful probe of gas fractions and ba…
Evaluating the flexibility of the MillenniumTNG galaxy formation model with multi-zoom re-simulations
Francisco Maion, Raul E. Angulo, Volker Springel +2
The authors develop a multi-zoom simulation suite based on the MillenniumTNG run to test how star‑formation and AGN feedback parameters affect the galaxy stellar‑mass function and…
Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models
Richard Stiskalek, Lucia A. Perez, Shy Genel +3
Learning cosmology from galaxy surveys requires large suites of simulations spanning the cosmological and astrophysical parameter space, yet hydrodynamical simulations of galaxy fo…
The evolution of the baryonic content and mass profiles of satellite galaxies in the MTNG simulations
Sergio Contreras, Raul E. Angulo, Giovanni Aricò +5
Empirical models often rely on key relations from the galaxy--halo connection to construct mock galaxy catalogues. These relations typically describe central galaxies more accurate…
Cosmological constraints from the small scale clustering of Emission Line Galaxies
Sara Ortega-Martinez, Raul E. Angulo, Sergio Contreras +7
Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (…
Cosmological constraints from galaxy clustering and galaxy-galaxy lensing with extended SubHalo Abundance Matching
Constance Mahony, Sergio Contreras, Raul E. Angulo +3
We present the first cosmological constraints from a joint analysis of galaxy clustering and galaxy-galaxy lensing using extended SubHalo Abundance Matching (SHAMe). We analyse ste…