2020MNRAS.497.3323P

Constructing dynamical models for interacting galaxies constrained by their observed structure and kinematics crucially depends on the correct choice of the values of their relative inclination (i) and viewing angle (θ) (the angle between the line of sight and the normal to the plane of their orbital motion). We construct Deep Convolutional Neural Network (DCNN) models to determine the i and θ of interacting galaxy pairs, using N-body + smoothed particle hydrodynamics (SPH) simulation data from the GalMer data base for training. GalMer simulates only a discrete set of i values (0°, 45°, 75°, and 90°) and almost all possible values of θ values in the range, [-90°, 90°]. Therefore, we have used classification for i parameter and regression for θ. In order to classify galaxy pairs based on their i values only, we first construct DCNN models for (i) 2-class (i = 0 °, 45°) (ii) 3-class (i = 0°, 45°, 90°) classification, obtaining F₁ scores of 99 per cent and 98 per cent respectively. Further, for a classification based on both i and θ values, we develop a DCNN model for a 9-class classification using different possible combinations of i and θ, and the F₁ score was 97 per cent. To estimate θ alone, we have used regression, and obtained a mean-squared error value of 0.12. Finally, we also tested our DCNN model on real data from Sloan Digital Sky Survey. Our DCNN models could be extended to determine additional dynamical parameters, currently determined by trial and error method.