Constraining the three-dimensional orbits of galaxies under ram pressure stripping with convolutional neural networks

Ram pressure stripping (RPS) of gas from disk galaxies has long been considered to play vital roles in galaxy evolution within groups and clusters. For a given density of intracluster medium (ICM) and a given velocity of a disk galaxy, RPS can be controlled by two angles (theta and phi) that define the angular relationship between the direction vector of the galaxy's three-dimensional (3D) motion within its host cluster and the galaxy's spin vector. We here propose a new method in which convolutional neutral networks (CNNs) are used to constrain theta and phi of disk galaxies under RPS. We first train a CNN by using ~10^5 synthesized images of gaseous distributions of the galaxies from numerous RPS models with different theta and phi. We then apply the trained CNN to a new test RPS model to predict theta and phi. The similarity between the correct and predicted theta and $\phi$ is measured by cosine similarity (cos-Theta) with cos-Theta =1 being perfectly accurate prediction. We show that the average cos-Theta among test models is ~0.95, which means that theta and phi can be constrained well by applying the CNN to the spatial distributions of their gas. This result suggests that if the ICM is in hydrostatic equilibrium (thus not moving), the 3D orbit of a disk galaxy within its host cluster can be constrained by the spatial distribution of the gas being stripped by RPS. We discuss the applications of the method to HI surveys such as WALLABY and SKA projects.