用人工智能寻找次晕性质中的普遍关系
Finding universal relations in subhalo properties with artificial intelligence
摘要：
我们使用一个通用的形式主义，旨在寻找在高维空间中的关系，以确定是否可以从其他内部属性，如速度色散，半径，或恒星形成率的subhalo的总质量进行预测。我们训练神经网络使用的数据从宇宙学和天体物理学与机器学习模拟（CAMELS）项目，并表明该模型可以预测的总质量的subhalo具有很高的精度：超过99%的subhalos有一个预测的质量在0.2德克斯的真实值。该网络表现出令人惊讶的外推特性，能够准确地预测任何类型的次晕包含任何类型的星系在任何红移的总质量，从不同的宇宙学，天体物理学模型，次网格物理学，体积和分辨率的模拟，表明该网络可能已经找到了一个普遍的关系。然后，我们使用不同的方法来找到近似的网络发现的关系方程，并推导出新的解析表达式，预测的总质量的subhalo从它的半径，速度色散，和最大圆周速度。我们表明，在某些制度下，解析表达式比神经网络更准确。我们解释的神经网络和近似的解析方程被连接到维里定理发现的关系。
英文摘要：
We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star-formation rate. We train neural networks using data from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project and show that the model can predict the total mass of a subhalo with high accuracy: more than 99% of the subhalos have a predicted mass within 0.2 dex of their true value. The networks exhibit surprising extrapolation properties, being able to accurately predict the total mass of any type of subhalo containing any kind of galaxy at any redshift from simulations with different cosmologies, astrophysics models, subgrid physics, volumes, and resolutions, indicating that the network may have found a universal relation. We then use different methods to find equations that approximate the relation found by the networks and derive new analytic expressions that predict the total mass of a subhalo from its radius, velocity dispersion, and maximum circular velocity. We show that in some regimes, the analytic expressions are more accurate than the neural networks. We interpret the relation found by the neural network and approximated by the analytic equation as being connected to the virial theorem.
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