CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912484837818368 |
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| author | Kannan, Sidharth Qiu, Tian Cuesta-Lazaro, Carolina Jeong, Haewon |
| author_facet | Kannan, Sidharth Qiu, Tian Cuesta-Lazaro, Carolina Jeong, Haewon |
| contents | Generative machine learning models have been demonstrated to be able to learn low dimensional representations of data that preserve information required for downstream tasks. In this work, we demonstrate that flow matching based generative models can learn compact, semantically rich latent representations of field level cold dark matter (CDM) simulation data without supervision. Our model, CosmoFlow, learns representations 32x smaller than the raw field data, usable for field level reconstruction, synthetic data generation, and parameter inference. Our model also learns interpretable representations, in which different latent channels correspond to features at different cosmological scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching Kannan, Sidharth Qiu, Tian Cuesta-Lazaro, Carolina Jeong, Haewon Cosmology and Nongalactic Astrophysics Machine Learning Generative machine learning models have been demonstrated to be able to learn low dimensional representations of data that preserve information required for downstream tasks. In this work, we demonstrate that flow matching based generative models can learn compact, semantically rich latent representations of field level cold dark matter (CDM) simulation data without supervision. Our model, CosmoFlow, learns representations 32x smaller than the raw field data, usable for field level reconstruction, synthetic data generation, and parameter inference. Our model also learns interpretable representations, in which different latent channels correspond to features at different cosmological scales. |
| title | CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching |
| topic | Cosmology and Nongalactic Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2507.11842 |