A Deep Learning Approach to Galaxy Cluster X-Ray Masses
A Deep Learning Approach to Galaxy Cluster X-Ray Masses
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DOI：
10.3847/1538-4357/ab14eb
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发表时间：
2018-10
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通讯作者：
M. Ntampaka;M. Ntampaka;J. Zuhone;D. Eisenstein;D. Nagai;A. Vikhlinin;L. Hernquist;F. Marinacci;D. Nelson;R. Pakmor;A. Pillepich;P. Torrey;M. Vogelsberger
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作者：
M. Ntampaka;M. Ntampaka;J. Zuhone;D. Eisenstein;D. Nagai;A. Vikhlinin;L. Hernquist;F. Marinacci;D. Nelson;R. Pakmor;A. Pillepich;P. Torrey;M. Vogelsberger
我们提出了一种从钱德拉模拟图像估计星系团质量的机器学习(ML)方法。我们使用卷积神经网络(CNN)，这是一种通常用于图像识别任务的深度最大似然工具。CNN是在我们的7896个钱德拉X射线模拟观测样本上进行训练和测试的，这些观测样本基于模拟中的329个大质量星系团。我们的CNN从光子计数的低分辨率空间分布中学习，而不使用光谱信息。尽管我们简化了忽略光谱信息的假设，但与模拟星系团的真实质量(−0.02Dex)相比，CNN估计的质量值显示出较小的偏差，并再现了低本征散射的星系团质量，在我们的最佳折叠处为8%，总体平均为12%。相比之下，更标准的核心切除光度法实现了15%-18%的散射。我们用一种受Google DeepDream启发的方法来解释结果，发现CNN忽略了星系团的中心区域，这些区域众所周知与质量有很高的散布。
We present a machine-learning (ML) approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep ML tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7896 Chandra X-ray mock observations, which are based on 329 massive clusters from the simulation. Our CNN learns from a low resolution spatial distribution of photon counts and does not use spectral information. Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (−0.02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all. In contrast, a more standard core-excised luminosity method achieves 15%–18% scatter. We interpret the results with an approach inspired by Google DeepDream and find that the CNN ignores the central regions of clusters, which are known to have high scatter with mass.