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arXiv 2501.16618astro-ph.CO
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使用神经网络预测宇宙时间尺度上的单个暗物质晕浓度

Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks

Tianchi Zhang, Tianxiang Mao, Wenxiao Xu, Guan Li

AI总结:

本研究利用大型宇宙学N体模拟的暗物质晕质量吸积历史训练神经网络,以预测给定红移下单个暗物质晕的浓度,其预测精度显著优于现有模型,为大型模拟提供了快速预测工具。

摘要:

暗物质晕的浓度与其质量吸积历史密切相关。我们将大型宇宙学N体模拟中的暗物质晕质量吸积历史作为神经网络的输入,训练其预测给定红移下单个暗物质晕的浓度。该训练好的模型在其他宇宙学模拟中表现有效,其实际与预测浓度之间的均方根误差在任何红移下均显著低于Zhao et al.和Giocoli et al.的模型。该模型可作为在大型宇宙学模拟中快速预测指定红移处暗物质晕浓度的有价值工具。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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