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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/2501.16618 |
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| _version_ | 1866913668013228032 |
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| author | Zhang, Tianchi Mao, Tianxiang Xu, Wenxiao Li, Guan |
| author_facet | Zhang, Tianchi Mao, Tianxiang Xu, Wenxiao Li, Guan |
| contents | The concentration of dark matter haloes is closely linked to their mass accretion history. We utilize the halo mass accretion histories from large cosmological N-body simulations as inputs for our neural networks, which we train to predict the concentration of individual haloes at a given redshift. The trained model performs effectively in other cosmological simulations, achieving the root mean square error between the actual and predicted concentrations that significantly lower than that of the model by Zhao et al. and Giocoli et al. at any redshift. This model serves as a valuable tool for rapidly predicting halo concentrations at specified redshifts in large cosmological simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16618 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks Zhang, Tianchi Mao, Tianxiang Xu, Wenxiao Li, Guan Cosmology and Nongalactic Astrophysics The concentration of dark matter haloes is closely linked to their mass accretion history. We utilize the halo mass accretion histories from large cosmological N-body simulations as inputs for our neural networks, which we train to predict the concentration of individual haloes at a given redshift. The trained model performs effectively in other cosmological simulations, achieving the root mean square error between the actual and predicted concentrations that significantly lower than that of the model by Zhao et al. and Giocoli et al. at any redshift. This model serves as a valuable tool for rapidly predicting halo concentrations at specified redshifts in large cosmological simulations. |
| title | Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2501.16618 |