Mon. Not. R. Astron. Soc., 485, 1924-1937 (2019/May-2)
Constraining the three-dimensional orbits of galaxies under ram pressure stripping with convolutional neural networks.
Abstract (from CDS):
Ram pressure stripping (RPS) of gas from disc 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 disc galaxy, RPS can be controlled by two angles (θ and ph) 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 θ and ph of disc galaxies under RPS. We first train a CNN by using ∼105 synthesized images of gaseous distributions of the galaxies from numerous RPS models with different θ and ph. We then apply the trained CNN to a new test RPS model to predict θ and ph. The similarity between the correct and predicted θ and ph is measured by cosine similarity (cos Θ) with cos Θ = 1 being perfectly accurate prediction. We show that the average cos Θ among test models is ≃0.95 (≃18° deviation), which means that θ and ph can be constrained by applying the CNN to the gaseous distributions. This result suggests that if the ICM is in hydrostatic equilibrium (thus not moving), the 3D orbit of a disc galaxy within its host cluster can be constrained by the spatial distribution of the gas being stripped by RPS. We discuss how this new method can be applied to H I studies of galaxies by ongoing and future large H I surveys such as the WALLABY and the SKA projects.