Daniel de Andres, W. Cui, G. Yepes, M. Petris, G. Aversano, A. Ferragamo, Federico De Luca, A. J. Munoz
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引用次数: 0
摘要
星系团由暗物质、气体和恒星组成。它们的暗物质部分约占总质量的80%,无法直接观测到,只能通过扩散气体和星系成员的分布来追踪。在这项工作中,我们的目标是通过训练深度学习模型,从模拟观测数据(即恒星、Sunyaev-Zeldovich 和 X 射线)中推断出星团的投影总质量分布。为此,我们从 "三百 "模拟中创建了一个多视图图像数据集,该数据集是训练机器学习模型的最佳选择。我们进一步研究了基于 U-Net 的深度学习架构,以考虑单输入和多输入模型。我们的研究表明,预测的质量分布与真实质量分布非常吻合。
Generating galaxy clusters mass density maps from mock multiview images via deep learning
Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster’s projected total mass distribution from mock observational data, i.e. stars, Sunyaev-Zeldovich, and X-ray, by training deep learning models. To this end, we have created a multiview images dataset from The Three Hundred simulation that is optimal for training Machine Learning models. We further study deep learning architectures based on the U-Net to account for single-input and multi-input models. We show that the predicted mass distribution agrees well with the true one.