Subhalo abundance matching and assembly bias in the EAGLE simulation
Jon\'as Chaves-Montero, Raul E. Angulo, Joop Schaye, Matthieu, Schaller, Robert A. Crain, Michelle Furlong, Tom Theuns

TL;DR
This study evaluates the effectiveness of subhalo abundance matching (SHAM) in the EAGLE simulation, showing it accurately predicts galaxy clustering on large scales but has limitations related to satellite fractions and assembly bias effects.
Contribution
The paper identifies the best subhalo property for SHAM, $V_{relax}$, and analyzes its performance and limitations in reproducing galaxy clustering and assembly bias in EAGLE.
Findings
$V_{relax}$ correlates strongly with galaxy stellar mass.
SHAM reproduces real-space clustering within uncertainties for scales >2 Mpc.
Clustering is overestimated below 2 Mpc due to satellite fraction discrepancies.
Abstract
Subhalo abundance matching (SHAM) is a widely-used method to connect galaxies with dark matter structures in numerical simulations. SHAM predictions agree remarkably well with observations, yet they still lack strong theoretical support. We examine the performance, implementation, and assumptions of SHAM using the EAGLE project simulations. We find that , the highest value of the circular velocity attained by a subhalo while it satisfies a relaxation criterion, is the subhalo property that correlates most strongly with galaxy stellar mass (). Using this parameter in SHAM, we retrieve the real-space clustering of EAGLE to within our statistical uncertainties on scales greater than Mpc for galaxies with . Conversely, clustering is overestimated by on scales below Mpc for galaxies with…
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