Weak Lensing Tomographic Redshift Distribution Inference for the Hyper Suprime-Cam Subaru Strategic Program three-year shape catalogue

TL;DR

This paper develops a Bayesian method combining galaxy photometry and spatial cross-correlations to accurately infer redshift distributions for the HSC Y3 weak lensing survey, accounting for systematic biases and cosmic variance.

Contribution

It introduces a combined Bayesian Hierarchical Inference approach that integrates photometric and spatial cross-correlation data for redshift distribution estimation in weak lensing.

Findings

Consistent redshift distributions from multiple methods.

Quantified the impact of cosmic variance on redshift inference.

Provided conservative priors for cosmological analyses.

Abstract

We present posterior sample redshift distributions for the Hyper Suprime-Cam Subaru Strategic Program Weak Lensing three-year (HSC Y3) analysis. Using the galaxies' photometry and spatial cross-correlations, we conduct a combined Bayesian Hierarchical Inference of the sample redshift distributions. The spatial cross-correlations are derived using a subsample of Luminous Red Galaxies (LRGs) with accurate redshift information available up to a photometric redshift of $z < 1.2$. We derive the photometry-based constraints using a combination of two empirical techniques calibrated on spectroscopic- and multiband photometric data that covers a spatial subset of the shear catalog. The limited spatial coverage induces a cosmic variance error budget that we include in the inference. Our cross-correlation analysis models the photometric redshift error of the LRGs to correct for systematic biases and statistical uncertainties. We demonstrate consistency between the sample redshift distributions derived using the spatial cross-correlations, the photometry, and the posterior of the combined analysis. Based on this assessment, we recommend conservative priors for sample redshift distributions of tomographic bins used in the three-year cosmological Weak Lensing analyses.