DOE OSTI · 2406505
Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
Abstract
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.
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Crenshaw, John Franklin (ORCID:0000000224953514), Kalmbach, J. Bryce (ORCID:0000000268255283), Gagliano, Alexander (ORCID:0000000349068447), Yan, Ziang (ORCID:0000000180435378), Connolly, Andrew J. (ORCID:0000000155768189), Malz, Alex I. (ORCID:0000000286761622), Schmidt, Samuel J. (ORCID:0000000250910470). 2024-07-23. Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows. https://doi.org/10.3847/1538-3881%2Fad54bf
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