Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918161902731264 |
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| author | Filipp, Andreas Nguyen, Tri Perreault-Levasseur, Laurence Rose, Jonah Lovell, Chris Payot, Nicolas Villaescusa-Navarro, Francisco Hezaveh, Yashar |
| author_facet | Filipp, Andreas Nguyen, Tri Perreault-Levasseur, Laurence Rose, Jonah Lovell, Chris Payot, Nicolas Villaescusa-Navarro, Francisco Hezaveh, Yashar |
| contents | Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass $M^{-1}_{\rm DM}$. To train the model, we use 1024 high-resolution hydrodynamical zoom-in simulations from the DREAMS suite. Mock observations are generated using Synthesizer, excluding gas particle contributions, and SHMFs are computed with the Rockstar halo finder. Our neural network takes as input both the galaxy images and the inverse DM mass. This method enables scalable, image-based predictions for the theoretical DM SHMFs of individual galaxies, facilitating direct comparisons with observational measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting the Subhalo Mass Functions in Simulations from Galaxy Images Filipp, Andreas Nguyen, Tri Perreault-Levasseur, Laurence Rose, Jonah Lovell, Chris Payot, Nicolas Villaescusa-Navarro, Francisco Hezaveh, Yashar Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass $M^{-1}_{\rm DM}$. To train the model, we use 1024 high-resolution hydrodynamical zoom-in simulations from the DREAMS suite. Mock observations are generated using Synthesizer, excluding gas particle contributions, and SHMFs are computed with the Rockstar halo finder. Our neural network takes as input both the galaxy images and the inverse DM mass. This method enables scalable, image-based predictions for the theoretical DM SHMFs of individual galaxies, facilitating direct comparisons with observational measurements. |
| title | Predicting the Subhalo Mass Functions in Simulations from Galaxy Images |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2510.14766 |