# Accurate Modeling of the Projected Galaxy Clustering in Photometric Surveys: I. Tests with Mock Catalogs

## TL;DR

This paper introduces a new method to accurately model galaxy clustering in photometric surveys by jointly constraining halo occupation and redshift errors, validated with mock catalogs.

## Contribution

The paper presents a novel approach that simultaneously constrains galaxy-halo connection and photometric redshift uncertainties using projected correlation functions.

## Key findings

- Successfully recovers galaxy bias within ~1σ
- Effectively constrains HOD models and redshift errors
- Performs well even with 10% catastrophic redshift errors

## Abstract

We develop a novel method to explore the galaxy-halo connection using the galaxy imaging surveys by modeling the projected two-point correlation function measured from the galaxies with reasonable photometric redshift measurements. By assuming a Gaussian form of the photometric redshift errors, we are able to simultaneously constrain the halo occupation distribution (HOD) models and the effective photometric redshift uncertainties. Tests with mock galaxy catalogs demonstrate that this method can successfully recover (within $\sim 1\sigma$) the intrinsic large-scale galaxy bias, as well as the HOD models and the effective photometric redshift uncertainty. This method also works well even for galaxy samples with 10 per cent catastrophic photometric redshift errors.