Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Journal Article · 22 July 2024 · The Astronomical Journal

DOI: https://doi.org/10.3847/1538-3881/ad54bf · OSTI ID:2406505

Crenshaw, John Franklin ; Kalmbach, J. Bryce ; Gagliano, Alexander ; Yan, Ziang ; Connolly, Andrew J. ; Malz, Alex I. ; Schmidt, Samuel J.

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, and the wider applicability of PZFlow for statistical modeling of any tabular data.

• Sponsoring Organization: USDOE
• Grant/Contract Number: NONE; SC0011665
• OSTI ID: 2406505
• Journal Information: The Astronomical Journal, Journal Name: The Astronomical Journal Journal Issue: 2 Vol. 168; ISSN 0004-6256
• Publisher: American Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English