AI 中文总结
该研究训练卷积神经网络（CNN），结合TNG50模拟数据与UNet架构，从星系冷CGM的二维发射图预测其天球平面速度，为理解CGM流动提供新途径。
AI 中文摘要
我们提出了一种预测星系冷星系际介质（CGM）中气体云天球平面速度的新方法。该方法使用在TNG50宇宙学模拟生成的模拟发射图上训练的卷积神经网络（CNN），并加入了与即将到来的观测设备一致的前向建模噪声特性。我们使用182个银河系/仙女座星系的类似星系，通过Cloudy模型生成Hα发射图，以及视线平均的二维速度图。采用UNet架构训练该CNN，以发射图作为输入，输出天球平面速度图，而这些速度图无法通过传统方法进行观测约束。定性来看，该模型通常能够推断出真实的整体流动方向。我们量化了高斯噪声对网络训练和预测能力的影响。在即将到来的望远镜（如MOTHRA）有望探测到的深度下，该网络对天球平面速度方向的典型均方根误差为0.3-0.5v_vir。这意味着具有足够深度的二维发射图将能够估计冷CGM气体的另外两个相空间维度，从而支持针对性的后续研究，并更好地理解整体CGM流动。
英文摘要
We present a novel approach to predicting plane-of-sky velocities of cold gas clouds in the circumgalactic medium (CGM) of galaxies. The method uses a convolutional neural network (CNN) trained on simulated emission maps derived from the TNG50 cosmological simulation, with forward modeled noise properties consistent with upcoming observational facilities. Using 182 Milky Way/Andromeda analog galaxies, we generate emission maps in H$α$ using $\texttt{Cloudy}$ models, as well as line-of-sight averaged 2D velocity maps. Using a UNet architecture, we train the CNN to take emission maps as input and return plane-of-sky velocity maps as output, which cannot be observationally constrained using traditional methods. Qualitatively, the model is generally able to infer the true overall flow direction. We quantify the effects of Gaussian noise on the network's training and predictive power. At depths expected to be probed by forthcoming telescopes such as MOTHRA, the network has a typical RMS error for the plane-of-sky velocity direction of $0.3-0.5 v_{vir}$. This implies that 2D emission maps of sufficient depths will be able to estimate two additional phase space dimensions of cold CGM gas, enabling targeted followup and a better understanding of overall CGM flows.
Comments18 pages, 8 figures
