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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/2507.18054 |
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| _version_ | 1866916861245915136 |
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| author | Chacón-Lavanderos, Jazhiel Gómez-Vargas, Isidro Menchaca-Mendez, Ricardo Vázquez, J. Alberto |
| author_facet | Chacón-Lavanderos, Jazhiel Gómez-Vargas, Isidro Menchaca-Mendez, Ricardo Vázquez, J. Alberto |
| contents | In this paper, we explore the use of a variational autoencoder (VAE), a deep generative model, to compress and generate images of dark matter density fields from $Λ$CDM like cosmological simulations. The VAE learns a compact, low-dimensional representation of the large-scale structure, enabling both accurate reconstruction and generation of statistically realistic samples. We evaluated the generated images by comparing their power spectra to those of real simulations and the theoretical $Λ$CDM prediction, finding strong agreement with the state-of-the-art simulations. In addition, the VAE provides a fast and scalable method for generating synthetic cosmological data, making it a valuable tool for data augmentation. These capabilities can accelerate the development and training of more advanced machine learning models for cosmological analysis, particularly in scenarios where large-scale simulations are computationally expensive. Our results highlight the potential for generative artificial intelligence as a practical bridge between physical modeling and modern deep learning in cosmology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Variational autoencoder for generating realistic $N$-body simulations for dark matter halos Chacón-Lavanderos, Jazhiel Gómez-Vargas, Isidro Menchaca-Mendez, Ricardo Vázquez, J. Alberto Cosmology and Nongalactic Astrophysics In this paper, we explore the use of a variational autoencoder (VAE), a deep generative model, to compress and generate images of dark matter density fields from $Λ$CDM like cosmological simulations. The VAE learns a compact, low-dimensional representation of the large-scale structure, enabling both accurate reconstruction and generation of statistically realistic samples. We evaluated the generated images by comparing their power spectra to those of real simulations and the theoretical $Λ$CDM prediction, finding strong agreement with the state-of-the-art simulations. In addition, the VAE provides a fast and scalable method for generating synthetic cosmological data, making it a valuable tool for data augmentation. These capabilities can accelerate the development and training of more advanced machine learning models for cosmological analysis, particularly in scenarios where large-scale simulations are computationally expensive. Our results highlight the potential for generative artificial intelligence as a practical bridge between physical modeling and modern deep learning in cosmology. |
| title | Variational autoencoder for generating realistic $N$-body simulations for dark matter halos |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2507.18054 |