Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911832379228160 |
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| author | de Andres, Daniel Cui, Weiguang Yepes, Gustavo De Petris, Marco Aversano, Gianmarco Ferragamo, Antonio De Luca, Federico Muñoz, A. Jiménez |
| author_facet | de Andres, Daniel Cui, Weiguang Yepes, Gustavo De Petris, Marco Aversano, Gianmarco Ferragamo, Antonio De Luca, Federico Muñoz, A. Jiménez |
| contents | Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80\% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster's projected total mass distribution from mock observational data, i.e. stars, Sunyaev-Zeldovich, and X-ray, by training deep learning models. To this end, we have created a multiview images dataset from {\sc{The Three Hundred}} simulation that is optimal for training Machine Learning models. We further study deep learning architectures based on the U-Net to account for single-input and multi-input models. We show that the predicted mass distribution agrees well with the true one. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning de Andres, Daniel Cui, Weiguang Yepes, Gustavo De Petris, Marco Aversano, Gianmarco Ferragamo, Antonio De Luca, Federico Muñoz, A. Jiménez Cosmology and Nongalactic Astrophysics Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80\% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster's projected total mass distribution from mock observational data, i.e. stars, Sunyaev-Zeldovich, and X-ray, by training deep learning models. To this end, we have created a multiview images dataset from {\sc{The Three Hundred}} simulation that is optimal for training Machine Learning models. We further study deep learning architectures based on the U-Net to account for single-input and multi-input models. We show that the predicted mass distribution agrees well with the true one. |
| title | Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2404.05400 |