Dark Matter Halo Merger Histories Beyond Cold Dark Matter: I - Methods and Application to Warm Dark Matter
Andrew J. Benson (1), Arya Farahi (2), Shaun Cole (3), Leonidas A., Moustakas (4), Adrian Jenkins (3), Mark Lovell (3), Rachel Kennedy (3), John, Helly (3), Carlos Frenk (3) ((1) Carnegie Observatories, Pasadena, CA, U.S.A,, (2) Michigan Center for Theoretical Physics

TL;DR
This paper introduces a new methodology for accurately modeling halo mass functions and merger histories in non-cold dark matter universes, validated against N-body simulations, highlighting key differences from cold dark matter scenarios.
Contribution
It develops a self-consistent extended Press-Schechter formalism for non-cold dark matter models, enabling rapid exploration of subhalo populations with improved accuracy.
Findings
Warm dark matter suppresses low-mass halos below the free-streaming scale.
Accurate solutions for the excursion set barrier are crucial for correct halo mass function truncation.
Mass accretion histories are similar between cold and warm dark matter when smooth accretion is included.
Abstract
We describe a methodology to accurately compute halo mass functions, progenitor mass functions, merger rates and merger trees in non-cold dark matter universes using a self-consistent treatment of the generalized extended Press-Schechter formalism. Our approach permits rapid exploration of the subhalo population of galactic halos in dark matter models with a variety of different particle properties or universes with rolling, truncated, or more complicated power spectra. We make detailed comparisons of analytically derived mass functions and merger histories with recent warm dark matter cosmological N-body simulations, and find excellent agreement. We show that, once the accretion of smoothly distributed matter is accounted for, coarse-grained statistics such as the mass accretion history of halos can be almost indistinguishable between cold and warm dark matter cases. However, the halo…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
