AI 中文总结
该研究针对测量银河系等星系群总质量困难的问题，提出用神经网络增强投影质量估计器的新框架，经模拟测试改进了质量高估问题，应用于银河系得出总质量，对星系群和星系团的暗物质相关量给出更严格限制。
AI 中文摘要
测量银河系和附近星系群的总质量很困难，因为经典动力学估计器依赖的卫星轨道几何假设在实际中很少满足，且可用的运动学示踪卫星星系很少。我们提出一个新框架，用在IllustrisTNG宇宙学模拟的数千个模拟星系群上训练的残差神经网络校正著名的投影质量估计器（PME）。针对不同卫星样本大小训练单独的网络。在模拟晕测试中，经典PME系统性高估晕质量，神经网络校正降低了高估程度。应用于银河系时，该方法给出了总质量。改进的PME对星系群和星系团的维里质量及暗物质率预测给出了更严格的限制。
英文摘要
Measuring the total mass of the Milky Way and nearby galaxy groups is difficult because classical dynamical estimators rely on assumptions about satellite orbital geometry that are rarely satisfied in practice, and because only a handful of satellite galaxies are typically available as kinematic tracers. We present a new framework that corrects the well-known Projected Mass Estimator (PME) using a residual neural network trained on thousands of simulated galaxy groups from the IllustrisTNG cosmological simulation. Separate networks are trained for each satellite sample size, from as few as 5 satellites up to 50, so that the correction automatically accounts for the statistical noise that dominates when only a small number of tracers is available. In tests on simulated halos, the classical PME systematically overestimates halo masses by factors of $M_{\rm proj}/M_{\rm true} = 1.30^{+0.72}_{-0.62}$ (using the 2D distance) and $1.46^{+0.97}_{-0.72}$ (using the 3D distance), with RMSE of 0.29 and 0.32 dex respectively. The neural-network correction reduces this to $M_{\rm proj}/M_{\rm true} = 1.02^{+0.30}_{-0.26}$ with an RMSE of 0.13 dex. Applied to the Milky Way, the method yields a total mass of $M_{\rm MW} = 1.144^{+0.399}_{-0.296}\times10^{12}\,M_\odot$, with estimates based on the brightest 5-10 satellites favoring a somewhat lower range of $(0.8$-$0.95)\times10^{12}\,M_\odot$. The modified PME gives a tighter constraint on the virial masses and the dark matter rate prediction in galaxy groups and clusters.
Comments12 pages; Comments are welcome!
