Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Crenshaw, John Franklin Kalmbach, J. Bryce Gagliano, Alexander Yan, Ziang Connolly, Andrew J. Malz, Alex I. Schmidt, Samuel J. Collaboration, The LSST Dark Energy Science |
| author_facet | Crenshaw, John Franklin Kalmbach, J. Bryce Gagliano, Alexander Yan, Ziang Connolly, Andrew J. Malz, Alex I. Schmidt, Samuel J. Collaboration, The LSST Dark Energy Science |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows Crenshaw, John Franklin Kalmbach, J. Bryce Gagliano, Alexander Yan, Ziang Connolly, Andrew J. Malz, Alex I. Schmidt, Samuel J. Collaboration, The LSST Dark Energy Science Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics 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. |
| title | Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2405.04740 |