The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader t...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad022a |
| _version_ | 1797391326834065408 |
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| author | Yueying Ni Shy Genel Daniel Anglés-Alcázar Francisco Villaescusa-Navarro Yongseok Jo Simeon Bird Tiziana Di Matteo Rupert Croft Nianyi Chen Natalí S. M. de Santi Matthew Gebhardt Helen Shao Shivam Pandey Lars Hernquist Romeel Dave |
| author_facet | Yueying Ni Shy Genel Daniel Anglés-Alcázar Francisco Villaescusa-Navarro Yongseok Jo Simeon Bird Tiziana Di Matteo Rupert Croft Nianyi Chen Natalí S. M. de Santi Matthew Gebhardt Helen Shao Shivam Pandey Lars Hernquist Romeel Dave |
| author_sort | Yueying Ni |
| collection | DOAJ |
| description | We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω _m , σ _8 , Ω _b ) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set. |
| first_indexed | 2024-03-08T23:31:21Z |
| format | Article |
| id | doaj.art-04449ab06ba345048ee2d12819457422 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-08T23:31:21Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-04449ab06ba345048ee2d128194574222023-12-14T11:40:14ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-01959213610.3847/1538-4357/ad022aThe CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA SuitesYueying Ni0https://orcid.org/0000-0001-7899-7195Shy Genel1https://orcid.org/0000-0002-3185-1540Daniel Anglés-Alcázar2https://orcid.org/0000-0001-5769-4945Francisco Villaescusa-Navarro3https://orcid.org/0000-0002-4816-0455Yongseok Jo4https://orcid.org/0000-0002-4728-6881Simeon Bird5https://orcid.org/0000-0001-5803-5490Tiziana Di Matteo6https://orcid.org/0000-0002-6462-5734Rupert Croft7https://orcid.org/0000-0003-0697-2583Nianyi Chen8https://orcid.org/0000-0001-6627-2533Natalí S. M. de Santi9https://orcid.org/0000-0002-4728-6881Matthew Gebhardt10https://orcid.org/0009-0003-0953-8931Helen Shao11https://orcid.org/0000-0002-0152-6747Shivam Pandey12https://orcid.org/0000-0001-5780-637XLars Hernquist13https://orcid.org/0000-0001-6950-1629Romeel Dave14https://orcid.org/0000-0003-2842-9434Harvard-Smithsonian Center for Astrophysics , 60 Garden Street, Cambridge, MA 02138, USA ; yueying.ni@cfa.harvard.edu; McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University , Pittsburgh, PA 15213, USACenter for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USA; Columbia Astrophysics Laboratory, Columbia University , 550 West 120th Street, New York, NY 10027, USACenter for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USA; Department of Physics, University of Connecticut , 196 Auditorium Road, U-3046, Storrs, CT 06269, USACenter for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USA; Department of Astrophysical Sciences, Princeton University , 4 Ivy Lane, Princeton, NJ 08544 USACenter for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USADepartment of Physics and Astronomy, University of California , Riverside, 900 University Avenue, Riverside, CA 92521, USAMcWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University , Pittsburgh, PA 15213, USA; NSF AI Planning Institute for Physics of the Future, Carnegie Mellon University , Pittsburgh, PA 15213, USAMcWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University , Pittsburgh, PA 15213, USA; NSF AI Planning Institute for Physics of the Future, Carnegie Mellon University , Pittsburgh, PA 15213, USAMcWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University , Pittsburgh, PA 15213, USACenter for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USA; Instituto de Física, Universidade de São Paulo , R. do Matão 1371, 05508-900, São Paulo, BrasilDepartment of Physics, University of Connecticut , 196 Auditorium Road, U-3046, Storrs, CT 06269, USADepartment of Astrophysical Sciences, Princeton University , 4 Ivy Lane, Princeton, NJ 08544 USADepartment of Physics, Columbia University , New York, NY 10027, USA; Department of Physics and Astronomy, University of Pennsylvania , Philadelphia, PA 19104, USAHarvard-Smithsonian Center for Astrophysics , 60 Garden Street, Cambridge, MA 02138, USA ; yueying.ni@cfa.harvard.eduInstitute for Astronomy, University of Edinburgh , Royal Observatory, Edinburgh EH9 3HJ, UKWe present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω _m , σ _8 , Ω _b ) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.https://doi.org/10.3847/1538-4357/ad022aLarge-scale structure of the universeHydrodynamical simulations |
| spellingShingle | Yueying Ni Shy Genel Daniel Anglés-Alcázar Francisco Villaescusa-Navarro Yongseok Jo Simeon Bird Tiziana Di Matteo Rupert Croft Nianyi Chen Natalí S. M. de Santi Matthew Gebhardt Helen Shao Shivam Pandey Lars Hernquist Romeel Dave The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites The Astrophysical Journal Large-scale structure of the universe Hydrodynamical simulations |
| title | The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites |
| title_full | The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites |
| title_fullStr | The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites |
| title_full_unstemmed | The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites |
| title_short | The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites |
| title_sort | camels project expanding the galaxy formation model space with new astrid and 28 parameter tng and simba suites |
| topic | Large-scale structure of the universe Hydrodynamical simulations |
| url | https://doi.org/10.3847/1538-4357/ad022a |
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