arXiv 2005.118192020-05astro-ph.CO

基于深度学习和流体动力学模拟的星系团质量估计

Galaxy cluster mass estimation with deep learning and hydrodynamical simulations

Z. Yan, A. J. Mead, L. Van Waerbeke, G. Hinshaw, I. G. McCarthy

备注：: 14 pages, 11 figures, 2 tables, Accepted for Publication on MNRAS
DOI：: 10.1093/mnras/staa3030

摘要：

在BAHAMAS流体动力学模拟中，我们评估了卷积神经网络（CNN）预测星系团质量的能力。我们训练四个独立的单通道网络使用：恒星质量，软X射线通量，辐射热X射线通量，和康普顿$y$参数作为观测示踪剂，分别。我们的训练集由从模拟生成的$\sim$ 4800个合成聚类图像组成，而另外的$\sim$ 3200个图像形成验证集和测试集，每个具有1600个图像。为了模拟真实观测，这些图像还包含位于星团前后50 Mpc范围内的不相关结构，以及投影中看到的仪器系统学，包括噪声和平滑。除了CNN的所有四个可观的，我们还训练一个“多通道”的CNN相结合的四个观测示踪剂。所有五个CNN的学习曲线都在1000个epoch内收敛。由此产生的预测是特别精确的晕质量范围内$10^{13.25}M_{\odot}<M<10^{14.5}M_{\odot}$，其中所有五个网络产生的平均质量偏差的顺序$\approx$ 1\%与分散的$\lesssim$ 20\%。用康普顿$y$参数图训练的网络产生最精确的预测。我们使用两个诊断测试来解释网络的行为，以确定哪些功能用于预测集群质量。用恒星质量图像训练的CNN检测星系（这并不奇怪），而用基于气体的示踪剂训练的CNN利用信号的形状来估计星系团的质量。

英文摘要：

We evaluate the ability of Convolutional Neural Networks (CNNs) to predict galaxy cluster masses in the BAHAMAS hydrodynamical simulations. We train four separate single-channel networks using: stellar mass, soft X-ray flux, bolometric X-ray flux, and the Compton $y$ parameter as observational tracers, respectively. Our training set consists of $\sim$4800 synthetic cluster images generated from the simulation, while an additional $\sim$3200 images form a validation set and a test set, each with 1600 images. In order to mimic real observation, these images also contain uncorrelated structures located within 50 Mpc in front and behind clusters and seen in projection, as well as instrumental systematics including noise and smoothing. In addition to CNNs for all the four observables, we also train a `multi-channel' CNN by combining the four observational tracers. The learning curves of all the five CNNs converge within 1000 epochs. The resulting predictions are especially precise for halo masses in the range $10^{13.25}M_{\odot}<M<10^{14.5}M_{\odot}$, where all five networks produce mean mass biases of order $\approx$1\% with a scatter of $\lesssim$20\%. The network trained with Compton $y$ parameter maps yields the most precise predictions. We interpret the network's behaviour using two diagnostic tests to determine which features are used to predict cluster mass. The CNN trained with stellar mass images detect galaxies (not surprisingly), while CNNs trained with gas-based tracers utilise the shape of the signal to estimate cluster mass.

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