论文标题
与半出现神经网络建模Galaxy-Halo连接
Modelling the galaxy-halo connection with semi-recurrent neural networks
论文作者
论文摘要
我们提出了一种人工神经网络设计，其中暗物质光环的过去和当前特性及其本地环境用于预测中央和卫星星系的时间分辨恒星形成历史和恒星金属性历史。使用Illustristng模拟中的数据，我们训练具有两个输入的基于张量的神经网络：具有暗物质光环的静态特性的标准层，例如光环质量和启动时间；以及带有过度密度和光晕质量积聚率的变量的经常性层，以多个时间步长评估，从$ 0 \ leq z \ sillsim 20 $。该模型成功地重现了Galaxy Halo连接的关键特征，例如中央和卫星星系，例如恒星至Halo质量关系，缩小和颜色双峰性。我们确定质量积聚史对于确定恒星形成历史的几何形状以及带有光环质量（例如缩小尺寸）的趋势至关重要，而环境变量是化学富集的重要指标。我们使用这些输出来计算光谱能量分布，并发现它们与Illustristng的等效结果非常匹配，从而恢复了观察性统计数据，例如颜色双峰性和质量图表。
We present an artificial neural network design in which past and present-day properties of dark matter halos and their local environment are used to predict time-resolved star formation histories and stellar metallicity histories of central and satellite galaxies. Using data from the IllustrisTNG simulations, we train a TensorFlow-based neural network with two inputs: a standard layer with static properties of the dark matter halo, such as halo mass and starting time; and a recurrent layer with variables such as overdensity and halo mass accretion rate, evaluated at multiple time steps from $0 \leq z \lesssim 20$. The model successfully reproduces key features of the galaxy halo connection, such as the stellar-to-halo mass relation, downsizing, and colour bimodality, for both central and satellite galaxies. We identify mass accretion history as crucial in determining the geometry of the star formation history and trends with halo mass such as downsizing, while environmental variables are important indicators of chemical enrichment. We use these outputs to compute optical spectral energy distributions, and find that they are well matched to the equivalent results in IllustrisTNG, recovering observational statistics such as colour bimodality and mass-magnitude diagrams.
