Understanding the nature of luminous red galaxies (LRGs): Connecting LRGs to central and satellite subhalos
Shogo Masaki (1), Chiaki Hikage (1), Masahiro Takada (2), David N., Spergel (2, 3), Naoshi Sugiyama (1,2), ((1) Nagoya U., (2) Kavli IPMU, (3), Princeton)

TL;DR
This paper introduces a new abundance matching method to create a mock catalog of luminous red galaxies (LRGs) that accurately reproduces various observed clustering and lensing properties in SDSS data, linking LRGs to their dark matter subhalos.
Contribution
The authors develop a novel abundance matching approach connecting high-redshift progenitor halos to present-day LRGs, successfully reproducing multiple observational measurements.
Findings
Mock catalog matches SDSS LRG clustering and lensing measurements
Reproduces halo occupation distribution for LRGs
Accurately models redshift-space distortions and Finger-of-God effects
Abstract
We develop a novel abundance matching method to construct a mock catalog of luminous red galaxies (LRGs) in SDSS, using catalogs of halos and subhalos in N-body simulations for a LCDM model. Motivated by observations suggesting that LRGs are passively-evolving, massive early-type galaxies with a typical age >5Gyr, we assume that simulated halos at z=2 (z2-halo) are progenitors for LRG-host subhalos observed today, and we label the most tightly bound particles in each progenitor z2-halo as LRG ``stars''. We then identify the subhalos containing these stars to z=0.3 (SDSS redshift) in descending order of the masses of z2-halos until the comoving number density of the matched subhalos becomes comparable to the measured number density of SDSS LRGs, n=10^{-4} (h/Mpc)^3. Once the above prescription is determined, our only free parameter is the number density of halos identified at z=2 and…
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.
