Evolution of Dark Matter Phase-Space Density Distributions in Equal-Mass Halo Mergers
Ileana M. Vass (U.Florida), Stelios Kazantzidis (CCAPP/OSU), Monica, Valluri (U. Michigan), Andrey V. Kravtsov (KICP/U.Chicago)

TL;DR
This study uses N-body simulations to analyze how dark matter halos' phase-space density distributions evolve during equal-mass mergers, revealing that mergers decrease phase-space density but retain memory of progenitor properties.
Contribution
It demonstrates that phase-space density profiles are robust and retain progenitor information after mergers, challenging the assumption of complete mixing in collisionless systems.
Findings
Mergers decrease coarse-grained phase-space density as expected.
Remnants preserve memory of progenitor inner slopes and shapes.
Phase-space density profiles are robust across various merger configurations.
Abstract
We use dissipationless N-body simulations to investigate the evolution of the true coarse-grained phase-space density distribution f(x,v) in equal-mass mergers between dark matter (DM) halos. The halo models are constructed with various asymptotic power-law indices ranging from steep cusps to core-like profiles and we employ the phase-space density estimator ``Enbid'' developed by Sharma & Steinmetz to compute f(x,v). The adopted force resolution allows robust phase-space density profile estimates in the inner ~1% of the virial radii of the simulated systems. We confirm that mergers result in a decrease of the coarse-grained phase-space density in accordance with expectations from Mixing Theorems for collisionless systems. We demonstrate that binary mergers between identical DM halos produce remnants that retain excellent memories of the inner slopes and overall shapes of the…
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