基于合成X射线和21厘米HI观测的CNN气体晕性质推断
CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
- University of California, Los Angeles（加州大学洛杉矶分校）
- University of Colorado（科罗拉多大学）
- Yale University（耶鲁大学）
- University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）
- Sorbonne Université（索邦大学）
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用CNN结合X射线与HI观测数据推断晕性质，发现多波段组合优于单波段，微热量计提升金属丰度推断1.75倍，并实现0.04 dex的群晕质量推断精度。
AI中文摘要：
量化多波段观测中的信息含量对于设定即将进行的X射线和21厘米HI射电巡天的曝光时间至关重要。我们在IllustrisTNG100和TNG300模拟的晕的模拟观测上训练卷积神经网络（CNNs），结合来自CCD或微热量计的软X射线通道数据与HI强度、速度和弥散图，以推断晕质量、气体分数、金属丰度和[O/Fe]丰度。多波段（X射线和HI）组合在推断气体分数方面始终优于单波段推断。在测量晕质量方面，X射线优于HI观测，但在测量具有显著冷气体含量的晕中的冷（T<10^5 K）气体分数时，两个波段的贡献相似。使用匹配的曝光时间，微热量计在金属丰度推断上比CCD提高了1.75倍，从而能够对最大的晕进行精确的[O/Fe]α增强测量。TNG300更大的体积允许推断星系群晕质量，发现使用100千秒X射线曝光时间的推断RMSE为0.04 dex。这些结果展示了深度学习如何评估开发仪器和设计针对这些昂贵的气体晕观测的巡天策略。
英文摘要：
Quantifying the information content in multi-wavelength observations is critical for setting exposure times for upcoming X-ray and 21-cm HI radio surveys. We train convolutional neural networks (CNNs) on mock observations of halos from the IllustrisTNG100 and TNG300 simulations, combining data from soft X-ray channels from a CCD or a microcalorimeter with HI intensity, velocity, and dispersion maps, to infer halo mass, gas fractions, metallicity, and [O/Fe] abundance. Multi-band (X-ray and HI) combinations consistently outperform single-band inference for gas fractions. X-ray outperforms HI observations for measuring halo mass, but both bands contribute similarly when measuring the cool (T<10^5 K) gas fraction in halos with significant cool gas content. Using matched exposure times, a micro-calorimeter improves metallicity inference over the CCD by a factor of 1.75, enabling precise measurements of [O/Fe] alpha-enhancement for the largest halos. The larger volume of TNG300 allows inference of group halo masses, finding an inference RMSE of 0.04 dex with a 100 ksec X-ray exposure time. These results demonstrate how deep learning can evaluate strategies for developing instruments and designing surveys for these expensive observations targeting gaseous halos.
