From 1,000 hydrodynamic simulations of the CAMELS project, each with a different value of the cosmological and astrophysical parameters, we generate 15,000 gas temperature maps. We use a state-of-the-art deep convolutional neural network to recover missing data from those maps. We mimic the missing data by applying regular and irregular binary masks that cover either $15\%$ or $30\%$ of the area of each map. We quantify the reliability of our results using two summary statistics: 1) the distance between the probability density functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the 2D power spectrum. We find an excellent agreement between the model prediction and the unmasked maps when using the power spectrum: better than $1\%$ for $k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic offset of $\sim5\%$ when covering $15\%$ of the maps while the results become unreliable when $30\%$ of the data is missing. The observed KS-test p-values favor the null hypothesis that the reconstructed and the ground-truth maps are drawn from the same underlying distribution when irregular masks are used. For regular-shaped masks on the other hand, we find a strong evidence that the two distributions do not match each other. Finally, we use the model, trained on gas temperature maps, to perform inpainting on maps from completely different fields such as gas mass, gas pressure, and electron density and also for gas temperature maps from simulations run with other codes. We find that visually, our model is able to reconstruct the missing pixels from the maps of those fields with great accuracy, although its performance using summary statistics depends strongly on the considered field.
Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network
深度卷积神经网络一直是图像生成和恢复的流行工具。这些网络的性能与从大型数据集中学习真实特征的能力有关。在这项工作中，我们应用了修复非高斯信号的问题，在毫米和亚毫米制度，特别是同步加速器和热尘埃排放的背景下，银河漫发射。它们都受到小角尺度的污染的影响，这是由于河外射电源（前者）和尘埃恒星形成星系（后者）。我们考虑了最近邻修复技术的性能，并将其与两种依赖于生成神经网络的新方法进行了比较。我们表明，生成网络能够以高置信度更一致地再现地面真实信号的统计特性。Python Inpainter for Cosmological and Astrophysical SOurces（PICASSO）是一个软件包，编码了本工作中描述的一套修复方法，并已公开提供。
Galaxy cluster temperature maps from joint X-ray and SZ maps with The Three Hundred hydrodynamical simulations
Sapienza Università di Roma（罗马大学）；INAF-Osservatorio Astronomico di Roma（罗马天文台）；Università degli studi di Napoli “Federico II”（那不勒斯费德里科二世大学）；INAF/IASF-Milano（米兰天体物理研究所）；Universidad Autónoma de Madrid（马德里自治大学）；Centro de Investigación Avanzada en Física Fundamental (CIAFF)（基础物理前沿研究中心）；INAF-Osservatorio Astronomico di Trieste（的里雅斯特天文台）；IFPU-Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；University of Michigan（密歇根大学）；Université Côte d’Azur（蔚蓝海岸大学）；Observatoire de la Côte d’Azur（蔚蓝海岸天文台）
本文利用The Three Hundred流体动力学模拟的合成星系团样本，系统验证了通过结合X射线密度图与毫米波压力图推断温度图的方法，发现该方法能高精度恢复质量加权温度和光谱类温度，并讨论了有效长度l_eff的建模及其对温度图恢复的影响。
