预测星团动力学图像（π- DOC）：预测球状星团质量、距离和年龄的深度学习框架
Predicting Images for the Dynamics Of stellar Clusters (π-DOC): a deep learning framework to predict mass, distance and age of globular clusters
摘要：
对于像球状星团（GCs）这样的简单系统，动力学质量的估计仍然有2倍的不确定性。这主要是由于标准动力学模型的过度简化，往往忽略了GC的长期演变的影响。在这里，我们介绍了一种新的方法来测量GC的动力学特性，基于深度学习框架和直接$N$体模拟的大量数据的组合。我们的算法$\texttt{$ π $-DOC}$（$\textit{Predicting Images for the Dynamics Of stellar Clusters}$）由两个卷积网络组成，经过训练可以学习观察到的GC光度图与其相关的质量分布，年龄和距离之间的非平凡变换。训练集是由V波段亮度和质量图构建为模拟观测$N$体模拟。对$\texttt{$ π $-DOC}$的检验表明，我们可以预测质量分布，每像素的平均误差为27%，年龄和距离的精度分别为1.5 Gyr和6 kpc。反过来，我们恢复的形状的质量-光的配置文件和其全球价值的平均误差为12%，这意味着我们有效地跟踪质量分离。与观测的初步比较表明，我们的算法是能够预测的动力学性质的GC的训练集的限制内。这些令人鼓舞的结果表明，我们的深度学习框架及其正向建模方法可以提供一种快速且适应性强的工具，与标准动态模型竞争。
英文摘要：
Dynamical mass estimates of simple systems such globular clusters (GCs) still suffer from up to a factor of 2 uncertainty. This is primarily due to the oversimplifications of standard dynamical models that often neglect the effects of the long-term evolution of GCs. Here, we introduce a new approach to measure the dynamical properties of GCs, based on the combination of a deep-learning framework and the large amount of data from direct $N$-body simulations. Our algorithm, $\texttt{$π$-DOC}$ ($\textit{Predicting Images for the Dynamics Of stellar Clusters}$) is composed of two convolutional networks, trained to learn the non-trivial transformation between an observed GC luminosity map and its associated mass distribution, age, and distance. The training set is made of V-band luminosity and mass maps constructed as mock observations from $N$-body simulations. The tests on $\texttt{$π$-DOC}$ demonstrate that we can predict the mass distribution with a mean error per pixel of 27%, and the age and distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we recover the shape of the mass-to-light profile and its global value with a mean error of 12%, which implies that we efficiently trace mass segregation. A preliminary comparison with observations indicates that our algorithm is able to predict the dynamical properties of GCs within the limits of the training set. These encouraging results demonstrate that our deep-learning framework and its forward modelling approach can offer a rapid and adaptable tool competitive with standard dynamical models.
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