# Testing the accuracy of clustering redshift with simulations

## TL;DR

This study assesses the precision of clustering-based redshift inference using simulations, demonstrating its potential for accurate redshift estimation and its advantages over traditional methods in upcoming cosmological surveys.

## Contribution

It provides a detailed evaluation of clustering redshift accuracy with simulations, highlighting its independence from representative spectroscopic samples and its application to individual galaxy redshifts.

## Key findings

- Achieves 0.1% mean redshift accuracy with specific galaxy densities.
- Demonstrates accurate individual redshift measurements with low bias and scatter.
- Builds tomographic bins with minimal bias, suitable for weak lensing analyses.

## Abstract

We explore the accuracy of the clustering-based redshift inference within the MICE2 simulation. This method uses the spatial clustering of galaxies between a spectroscopic reference sample and an unknown sample. The goal of this study is to give a preview of the redshift accuracy one can reach with this method. To do so, we first highlight the requirements of this technique in term of number of objects in both the reference and unknown samples. We also confirm that this method does not require a representative spectroscopic sample for calibration. We estimate that a density of spectroscopic objects of $10^{-5}$ arcmin$^{-2}$ per redshift bin of width $\delta z = 0.01$ over $9000 \ \text{deg}^{2}$ allows to reach 0.1 \% accuracy in the mean redshift for a galaxy density compatible with next generation of cosmological surveys. This number is compatible with the density of the Quasi Stellar Objects in BOSS. Second we demonstrate our ability to measure individual redshifts for galaxies independently from the photometric redshifts procedure. The resulting individual clustering redshifts have a bias=$-0.001$, an outlier fraction of $\eta=3.57\%$ and a scatter of $\sigma=0.027$ to $i<25$. The advantage of this procedure is threefold: i) it allows the use of clustering redshifts for any field in astronomy, ii) it allows the possibility to combine photometric and clustering based redshifts to get an improved redshift estimation, iii) it allows the use of cluster-$z$ to define tomographic bins for weak lensing. Finally we explore this last option and build 5 clustering redshift selected tomographic bins from redshift 0.2 to 1. We found a bias on the mean redshift estimate of $0.002$ per bin.