论文标题
关于银河系簇的深度学习动态质量估计的近似贝叶斯不确定性
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
论文作者
论文摘要
我们研究了使用卷积神经网络（CNN）重建贝叶斯不确定性对星系簇的动态质量估计的方法。我们讨论了近似贝叶斯神经网络的统计背景，并证明了如何使用变异推理技术来对各种深神经体系结构进行计算可牵引的后验估计。我们探讨了各种模型设计和统计假设如何在集群质量估计的背景下影响预测准确性和不确定性重建。我们使用源自Multidark Simulation和Universemachine目录的模拟群集观测目录来测量模型后验恢复的质量。我们表明，近似贝叶斯CNN会产生高度准确的动力簇质量后期。这些型号的后代是集群质量的对数正态，并收回$ 68 \％$ $和$ 90 \％$ $置信区间，至其测量值的$ 1 \％$。我们注意到，这种动态质量后期的严格建模对于使用群集丰度测量来限制宇宙学参数是必要的。
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.
