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arXiv 2203.03360astro-ph.GAastro-ph.CO
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机器学习识别模拟星系团中的ICL和BCG

Machine Learning to identify ICL and BCG in simulated galaxy clusters

I. Marini, S. Borgani, A. Saro, G. Murante, G. L. Granato, C. Ragone-Figueroa, G. Taffoni

摘要:

如今,机器学习技术为传统方法需要密集计算资源的分类问题提供了快速有效的解决方案。我们研究了使用监督随机森林分类恒星在减去成员星系后的模拟星系团。这些动态不同的组件被解释为Brilliant星团星系(BCG)和星团内光(ICL)中的恒星的个体属性。我们采用匹配的恒星目录(从不同的动力学性质的BCG和ICL)的29个模拟集群从DIANOGA设置训练和测试分类器。输入特征是簇质量、归一化粒子簇中心距离和静止帧速度。该模型被发现,以正确地识别大部分的恒星,而在BCG的外围,在那里的两个组件的物理特性之间的差异不太明显,表现出较大的误差。我们调查的鲁棒性的分类器的数值分辨率,红移的依赖性(高达$z=1$),包括天体物理模型。我们声称,我们的分类器提供了一致的结果,在模拟$z<1$,在不同的分辨率水平和显着不同的子网格模型。相空间结构的检查,以评估是否恢复的恒星组件的一般属性:(i)BCG为主的和ICL为主的区域之间的过渡半径被确定在$0.04$ \r 200;(ii)BCG外围($> 0.1$ \r 200)是显着的分类过程中的不确定性的影响。总之,这项工作表明了使用机器学习来加速模拟中计算昂贵的分类的重要性。

英文摘要:

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify stars in simulated galaxy clusters after subtracting the member galaxies. These dynamically different components are interpreted as the individual properties of the stars in the Brightest Cluster Galaxy (BCG) and IntraCluster Light (ICL). We employ matched stellar catalogues (built from the different dynamical properties of BCG and ICL) of 29 simulated clusters from the DIANOGA set to train and test the classifier. The input features are cluster mass, normalized particle cluster-centric distance, and rest-frame velocity. The model is found to correctly identify most of the stars, while the larger errors are exhibited at the BCG outskirt, where the differences between the physical properties of the two components are less obvious. We investigate the robustness of the classifier to numerical resolution, redshift dependence (up to $z=1$), and included astrophysical models. We claim that our classifier provides consistent results in simulations for $z<1$, at different resolution levels and with significantly different subgrid models. The phase-space structure is examined to assess whether the general properties of the stellar components are recovered: (i) the transition radius between BCG-dominated and ICL-dominated region is identified at $0.04$ \r200; (ii) the BCG outskirt ($> 0.1$ \r200) is significantly affected by uncertainties in the classification process. In conclusion, this work suggests the importance of employing Machine Learning to speed up a computationally expensive classification in simulations.

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