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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.17087 |
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| _version_ | 1866916905842900992 |
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| author | Riveros, Julieth Katherine Saavedra, Paola Hortua, Hector J. Garcia-Farieta, Jorge Enrique Olier, Ivan |
| author_facet | Riveros, Julieth Katherine Saavedra, Paola Hortua, Hector J. Garcia-Farieta, Jorge Enrique Olier, Ivan |
| contents | Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-linear cosmic web. We address this challenge by exploring conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods. Our results demonstrate the power of diffusion models to accurately reproduce the matter power spectra and bispectra, even for unseen configurations. They also offer a significant speed-up with slightly reduced accuracy, when flow-based reconstructing the probability distribution function, but they struggle with higher-order statistics. To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. Our findings offer a powerful tool for exploring deviations from standard gravity, combining high precision with reduced computational cost, thus paving the way for more comprehensive and efficient cosmological analyses |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies Riveros, Julieth Katherine Saavedra, Paola Hortua, Hector J. Garcia-Farieta, Jorge Enrique Olier, Ivan Cosmology and Nongalactic Astrophysics Artificial Intelligence Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-linear cosmic web. We address this challenge by exploring conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods. Our results demonstrate the power of diffusion models to accurately reproduce the matter power spectra and bispectra, even for unseen configurations. They also offer a significant speed-up with slightly reduced accuracy, when flow-based reconstructing the probability distribution function, but they struggle with higher-order statistics. To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. Our findings offer a powerful tool for exploring deviations from standard gravity, combining high precision with reduced computational cost, thus paving the way for more comprehensive and efficient cosmological analyses |
| title | Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies |
| topic | Cosmology and Nongalactic Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2502.17087 |