Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

TL;DR

This paper introduces PZFlow, a probabilistic modeling tool using normalizing flows to simulate galaxy catalogs with true redshift posteriors, aiding validation of photometric redshift estimators for cosmology.

Contribution

PZFlow provides a novel framework for forward modeling galaxy catalogs with true redshift posteriors, enabling validation of photo-z estimators and broader statistical data modeling.

Findings

PZFlow successfully simulates galaxy catalogs with realistic properties.

Ensemble normalizing flows improve photometric redshift estimation.

Validation of photo-z pipelines is enhanced using true redshift posteriors.

Abstract

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of "true" redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration (DESC), and the wider applicability of PZFlow for statistical modeling of any tabular data.