Reconstructing redshift distributions with photometric galaxy clustering

TL;DR

This paper introduces an improved self-calibration method using photometric galaxy clustering to accurately reconstruct redshift distributions, overcoming previous limitations and matching external calibration accuracy for cosmological surveys.

Contribution

An enhanced self-calibration algorithm that effectively handles heteroskedastic weights and noisy data, broadening its applicability for cosmological redshift distribution reconstruction.

Findings

Accurately reconstructs redshift distributions for mock data.

Results are comparable to state-of-the-art external calibration.

Method expands the use of photometric clustering in cosmology.

Abstract

The accurate determination of the true redshift distributions in tomographic bins is critical for cosmological constraints from photometric surveys. The proposed redshift self-calibration method, which utilizes the photometric galaxy clustering alone, is highly convenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples in external calibration. However, the imperfection of the theoretical approximation on broad bins as well as the flaw of the algorithm in previous work risk the accuracy and application of the method. In this paper, we propose the improved self-calibration algorithm that incorporates novel update rules, which effectively accounts for heteroskedastic weights and noisy data with negative values. The improved algorithm greatly expands the application range of self-calibration method and accurately reconstructs the redshift distributions for various mock data. Using the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument (DESI) survey, we find that the reconstructed results are comparable to the state-of-the-art external calibration. This suggests the exciting prospect of using photometric galaxy clustering to reconstruct redshift distributions in the cosmological analysis of survey data.