Elucidating Galaxy Assembly Bias in SDSS
Andr\'es N. Salcedo, Ying Zu, Youcai Zhang, Huiyuan Wang, Xiaohu Yang,, Yiheng Wu, Yipeng Jing, Houjun Mo, and David H. Weinberg

TL;DR
This study uses advanced constrained simulations to accurately measure galaxy assembly bias in SDSS data, finding no significant bias and demonstrating a method that can improve galaxy-halo modeling in future surveys.
Contribution
The paper introduces a new method combining constrained simulations and an extended HOD model to accurately constrain galaxy assembly bias, reducing cosmic variance effects.
Findings
No significant galaxy assembly bias detected in SDSS for galaxies above 10^{10.2} M_sun
Constrained simulations effectively remove degeneracies caused by cosmic variance
Method can be applied to future large-scale surveys like DESI and PFS
Abstract
We investigate the level of galaxy assembly bias in the Sloan Digital Sky Survey (SDSS) main galaxy sample using ELUCID, a state-of-the-art constrained simulation that accurately reconstructed the initial density perturbations within the SDSS volume. On top of the ELUCID haloes, we develop an extended HOD model that includes the assembly bias of central and satellite galaxies, parameterized as and , respectively, to predict a suite of one- and two-point observables. In particular, our fiducial constraint employs the probability distribution of the galaxy number counts measured on scales and the projected cross-correlation functions of quintiles of galaxies selected by with our entire galaxy sample. We perform extensive tests of the efficacy of our method by fitting the same observables to mock…
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.
