基于深度学习的星系团动态质量估计的近似贝叶斯解
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
摘要：
我们研究的方法重建贝叶斯不确定性的动态质量估计的星系团使用卷积神经网络（CNN）。我们讨论了近似贝叶斯神经网络的统计背景，并演示了如何使用变分推理技术来为各种深度神经架构执行计算上易于处理的后验估计。我们探讨了各种模型设计和统计假设如何影响集群质量估计的预测精度和不确定性重建。我们使用来自MultiDark模拟和UniverseMachine目录的模拟集群观察目录来测量模型后验恢复的质量。我们表明，近似贝叶斯CNN产生高度准确的动态集群质量后验。这些模型后验在聚类质量方面是对数正态的，并将$68\%$和$90\%$置信区间恢复到其测量值的$1\%$范围内。我们注意到，这种严格的动力学质量后验模型是必要的使用集群丰度测量约束宇宙学参数。
英文摘要：
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of approximate Bayesian neural networks and demonstrate how variational inference techniques can be used to perform computationally tractable posterior estimation for a variety of deep neural architectures. We explore how various model designs and statistical assumptions impact prediction accuracy and uncertainty reconstruction in the context of cluster mass estimation. We measure the quality of our model posterior recovery using a mock cluster observation catalog derived from the MultiDark simulation and UniverseMachine catalog. We show that approximate Bayesian CNNs produce highly accurate dynamical cluster mass posteriors. These model posteriors are log-normal in cluster mass and recover $68\%$ and $90\%$ confidence intervals to within $1\%$ of their measured value. We note how this rigorous modeling of dynamical mass posteriors is necessary for using cluster abundance measurements to constrain cosmological parameters.
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