# Understanding the non-linear clustering of high redshift galaxies

## TL;DR

This paper enhances the modeling of high-redshift galaxy clustering by incorporating non-linear dark matter halo clustering, significantly improving agreement with observations and providing insights into galaxy formation and cosmology.

## Contribution

It introduces a non-linear halo bias model for Lyman break galaxies, resolving discrepancies with observations and improving clustering predictions at high redshift.

## Key findings

- Non-linear halo bias increases predicted LBG clustering on quasi-linear scales.
- Model predictions align better with observed LBG clustering data.
- Clustering strength of LBGs grows with luminosity and redshift.

## Abstract

We incorporate the non-linear clustering of dark matter halos, as modelled by Jose et al. (2016) into the halo model to better understand the clustering of Lyman break galaxies (LBGs) in the redshift range $z=3-5$. We find that, with this change, the predicted LBG clustering increases significantly on quasi-linear scales ($0.1 \leq r\,/\,h^{-1} \,{\rm Mpc} \leq 10$) compared to that in the linear halo bias model. This in turn results in an increase in the clustering of LBGs by an order of magnitude on angular scales $5" \leq \theta \leq 100"$. Remarkably, the predictions of our new model on the whole remove the systematic discrepancy between the linear halo bias predictions and the observations. The correlation length and large scale galaxy bias of LBGs are found to be significantly higher in the non-linear halo bias model than in the linear halo bias model. The resulting two-point correlation function retains an approximate power-law form in contrast with that computed using the linear halo bias theory. We also find that the non-linear clustering of LBGs increases with increasing luminosity and redshift. Our work emphasizes the importance of using non-linear halo bias in order to model the clustering of high-z galaxies to probe the physics of galaxy formation and extract cosmological parameters reliably.