Predicting the Kinematics of the Cold Circumgalactic Medium from its Morphology using Convolutional Neural Networks
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$\alpha$ 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.