Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

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.

19 pages, 13 figures, submitted to AJ

References in corpus (14)

• Array Programming with NumPy
• The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
• Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
• Photometric redshift analysis in the Dark Energy Survey Science Verification data
• Precision photometric redshift calibration for galaxy-galaxy weak lensing
• Photometric Redshifts for Next-Generation Surveys
• The LSST DESC DC2 Simulated Sky Survey
• Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies
• Photometric Redshifts with the LSST II: The Impact of Near-Infrared and Near-Ultraviolet Photometry
• Photometric Redshift Uncertainties in Weak Gravitational Lensing Shear Analysis: Models and Marginalization
• Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
• The Simulated Catalogue of Optical Transients and Correlated Hosts (SCOTCH)
• Improving Photometric Redshift Estimates with Training Sample Augmentation
• The sensitivity of GPz estimates of photo-z posterior PDFs to realistically complex training set imperfections

Cited by in corpus (12)

• Impact of stellar population synthesis choices on forward modelling-based redshift distribution estimates
• pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
• Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey
• Galaxy Imaging with Generative Models: Insights from a Two-Models Framework
• pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
• Modelling the selection of galaxy groups with end to end simulations
• Regression for Astronomical Data with Realistic Distributions, Errors and Non-linearity
• Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference
• Photometric Redshift Estimation Using Scaled Ensemble Learning
• Optimizing the Roman Space Telescope High-Latitude Wide Area Survey for mitigating chromatic PSF effects on shear measurement
• TOPz: Photometric redshifts using template fitting applied to the GAMA survey
• GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference