A Composite Likelihood Approach for Inference under Photometric Redshift Uncertainty

TL;DR

This paper introduces a likelihood-based inference method that combines galaxy photometry and cross-correlations with spectroscopic samples to accurately calibrate redshift distributions in large photometric surveys like LSST.

Contribution

The paper presents a scalable, integrated likelihood framework for redshift calibration that incorporates two-point functions and cross-correlations, addressing key statistical challenges.

Findings

Achieves redshift mean calibration accuracy of 0.002(1+z)

Demonstrates method effectiveness on CosmoDC2 simulations

Supports large-scale survey requirements like LSST-Y1

Abstract

Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1+z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.