| Autor: |
John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz, Samuel J. Schmidt, The LSST Dark Energy Science Collaboration |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
The Astronomical Journal, Vol 168, Iss 2, p 80 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1538-3881 |
| DOI: |
10.3847/1538-3881/ad54bf |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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