Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference
We present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
|
| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/ac9583 |
| _version_ | 1797701418314891264 |
|---|---|
| author | Justin Alsing Hiranya Peiris Daniel Mortlock Joel Leja Boris Leistedt |
| author_facet | Justin Alsing Hiranya Peiris Daniel Mortlock Joel Leja Boris Leistedt |
| author_sort | Justin Alsing |
| collection | DOAJ |
| description | We present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data model characterizing the observation and calibration processes for a given survey; and explicit treatment of selection cuts, both into the main analysis sample and for the subsequent sorting into tomographic redshift bins. This approach has the appeal that it does not rely on spectroscopic calibration data, provides explicit control over modeling assumptions and builds a direct bridge between photo- z inference and galaxy evolution physics. In addition to redshift distributions, forward modeling provides a framework for drawing robust inferences about the statistical properties of the galaxy population more generally. We demonstrate the utility of forward modeling by estimating the redshift distributions for the Galaxy And Mass Assembly (GAMA) survey and the Vimos VLT Deep Survey (VVDS), validating against their spectroscopic redshifts. Our baseline model is able to predict tomographic redshift distributions for GAMA and VVDS with respective biases of Δ z ≲ 0.003 and Δ z ≃ 0.01 on the mean redshift—comfortably accurate enough for Stage III cosmological surveys—without any hyperparameter tuning (i.e., prior to doing any fitting to those data). We anticipate that with additional hyperparameter fitting and modeling improvements, forward modeling will provide a path to accurate redshift distribution inference for Stage IV surveys. |
| first_indexed | 2024-03-12T04:35:09Z |
| format | Article |
| id | doaj.art-c69a9eae35654970868b114a1940ba83 |
| institution | Directory Open Access Journal |
| issn | 0067-0049 |
| language | English |
| last_indexed | 2024-03-12T04:35:09Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj.art-c69a9eae35654970868b114a1940ba832023-09-03T09:56:33ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492023-01-0126422910.3847/1538-4365/ac9583Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution InferenceJustin Alsing0https://orcid.org/0000-0003-4618-3546Hiranya Peiris1https://orcid.org/0000-0002-2519-584XDaniel Mortlock2https://orcid.org/0000-0002-0041-3783Joel Leja3https://orcid.org/0000-0001-6755-1315Boris Leistedt4https://orcid.org/0000-0002-3962-9274Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University , Stockholm SE-106 91, Sweden ; justin.alsing@fysik.su.seOskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University , Stockholm SE-106 91, Sweden ; justin.alsing@fysik.su.se; Department of Physics and Astronomy, University College London , Gower Street, London, WC1E 6BT, UKOskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University , Stockholm SE-106 91, Sweden ; justin.alsing@fysik.su.se; Department of Physics, Imperial College London , Blackett Laboratory, Prince Consort Road, London, SW7 2AZ, UK; Department of Mathematics, Imperial College London , London, SW7 2AZ, UKDepartment of Astronomy & Astrophysics, The Pennsylvania State University , University Park, PA 16802, USA; Institute for Computational & Data Sciences, The Pennsylvania State University , University Park, PA 16802, USA; Institute for Gravitation and the Cosmos, The Pennsylvania State University , University Park, PA 16802, USADepartment of Physics, Imperial College London , Blackett Laboratory, Prince Consort Road, London, SW7 2AZ, UKWe present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data model characterizing the observation and calibration processes for a given survey; and explicit treatment of selection cuts, both into the main analysis sample and for the subsequent sorting into tomographic redshift bins. This approach has the appeal that it does not rely on spectroscopic calibration data, provides explicit control over modeling assumptions and builds a direct bridge between photo- z inference and galaxy evolution physics. In addition to redshift distributions, forward modeling provides a framework for drawing robust inferences about the statistical properties of the galaxy population more generally. We demonstrate the utility of forward modeling by estimating the redshift distributions for the Galaxy And Mass Assembly (GAMA) survey and the Vimos VLT Deep Survey (VVDS), validating against their spectroscopic redshifts. Our baseline model is able to predict tomographic redshift distributions for GAMA and VVDS with respective biases of Δ z ≲ 0.003 and Δ z ≃ 0.01 on the mean redshift—comfortably accurate enough for Stage III cosmological surveys—without any hyperparameter tuning (i.e., prior to doing any fitting to those data). We anticipate that with additional hyperparameter fitting and modeling improvements, forward modeling will provide a path to accurate redshift distribution inference for Stage IV surveys.https://doi.org/10.3847/1538-4365/ac9583Redshift surveysGalaxy photometryGalaxy stellar contentGalaxy evolutionCosmological parameters from large-scale structureGravitational lensing |
| spellingShingle | Justin Alsing Hiranya Peiris Daniel Mortlock Joel Leja Boris Leistedt Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference The Astrophysical Journal Supplement Series Redshift surveys Galaxy photometry Galaxy stellar content Galaxy evolution Cosmological parameters from large-scale structure Gravitational lensing |
| title | Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference |
| title_full | Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference |
| title_fullStr | Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference |
| title_full_unstemmed | Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference |
| title_short | Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference |
| title_sort | forward modeling of galaxy populations for cosmological redshift distribution inference |
| topic | Redshift surveys Galaxy photometry Galaxy stellar content Galaxy evolution Cosmological parameters from large-scale structure Gravitational lensing |
| url | https://doi.org/10.3847/1538-4365/ac9583 |
| work_keys_str_mv | AT justinalsing forwardmodelingofgalaxypopulationsforcosmologicalredshiftdistributioninference AT hiranyapeiris forwardmodelingofgalaxypopulationsforcosmologicalredshiftdistributioninference AT danielmortlock forwardmodelingofgalaxypopulationsforcosmologicalredshiftdistributioninference AT joelleja forwardmodelingofgalaxypopulationsforcosmologicalredshiftdistributioninference AT borisleistedt forwardmodelingofgalaxypopulationsforcosmologicalredshiftdistributioninference |