论文标题
基于3D卷积神经网络的动态星系簇质量的基于仿真的推断
Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
论文作者
论文摘要
我们使用卷积神经网络提出了一个基于模拟的推理框架，从其观察到的3D投影相位空间分布中推断出动态的星系簇，该分布由天空中预计的星系位置及其视线速度组成。通过在此基于模拟的推理框架内制定质量估计问题，我们能够以直接且健壮的方式量化推断质量的不确定性。我们生成了一个逼真的模拟目录，模仿了Sloan Digital Sky Survey（SDSS）Legacy Spectroscocic观察结果（主要的星系样本），用于红移$ Z \ Lessim 0.09 $，并明确说明了Interloper（非成员）为群集质量估算实际观察的挑战。我们的方法构成了第一个基于机器学习的第一个基于最佳的机器学习的利用，以推导动态群集质量的整个3D投影相位空间分布（包括病毒化和输入群集区域）。我们还首次提出了基于仿真的推理机械的应用，以获得SDSS遗产调查中发现的大约800美元的Galaxy群集的动态质量，并表明所得的质量估计与文献的质量测量一致。
We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations (the main galaxy sample) for redshifts $z \lesssim 0.09$ and explicitly illustrate the challenges posed by interloper (non-member) galaxies for cluster mass estimation from actual observations. Our approach constitutes the first optimal machine learning-based exploitation of the information content of the full 3D projected phase-space distribution, including both the virialized and infall cluster regions, for the inference of dynamical cluster masses. We also present, for the first time, the application of a simulation-based inference machinery to obtain dynamical masses of around $800$ galaxy clusters found in the SDSS Legacy Survey, and show that the resulting mass estimates are consistent with mass measurements from the literature.
