Non-Separable Halo Bias from High-Redshift Galaxy Clustering
Emy Mons, Vipul Prasad Maranchery, M. S. Suryan Sivadas, Charles Jose

TL;DR
This paper tests the assumption of halo bias separability in galaxy clustering models and finds significant deviations at high redshifts on quasi-linear scales, impacting the interpretation of galaxy-halo connections.
Contribution
It introduces a method to quantify halo bias separability deviations and demonstrates their significance at high redshifts using simulations and observational data.
Findings
Significant departures from bias separability at high redshifts on quasi-linear scales.
Deviations lead to a suppression of halo cross-correlations by up to a factor of 2.
High-redshift galaxy cross-correlations can probe non-separability to improve galaxy-halo models.
Abstract
The halo model provides a powerful framework for interpreting galaxy clustering by linking the spatial distribution of dark matter haloes to the underlying matter distribution. A key assumption within the halo bias approximation of the halo model is that, on sufficiently large scales, the halo bias between two halo populations is a separable function of the mass of each population. In this work, we test the validity of this approximation on quasi-linear scales using both simulations and observational data across a broad range of halo masses and redshifts. In particular, we define a separability function based on halo or galaxy cross-correlations to quantify deviations from halo bias separability, and measure it from N-body simulations. We find significant departures from separability on quasi-linear scales (\(\sim 1\text{--}5\,\mathrm{Mpc}\)) at high redshifts (\(z \geq 3\)), leading to…
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.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Statistical Mechanics and Entropy
