Estimating dynamical parameters of two interacting galaxies using Deep Learning

The science behind galaxy interaction and mergers has a fundamental role and gives us an insight into galaxy formation and its evolution. Fluctuating angular momentum is responsible for extraordinary events like polar rings, tidal tails, and ripples. Various parameters like the mass ratio of the interacting galaxy, orbital parameters, mass distribution, and morphologies are required to study different phenomena related to galaxy interactions. Convolutional Neural Networks (CNN) are widely used to predict image data. Thus, we used CNN as our approach to the problem. In this work, we will use data from state-of-the-art magneto-hydrodynamic simulations of galaxy mergers from the GalMer database at different dynamical parameters using image snapshots of merging pairs of galaxies and feeding them to our Deep Learning model. The dynamical parameters we are aiming for; would be spin, relative inclination (i), viewing angle (θ), and azimuthal angle (φ). We aim to download bulk data using the web scraping method. Here the model can predict the continuous and exact values of the dynamical parameters. We have achieved a 0.9986 R-squared value and a mean absolute error of 0.4348 on testing data. In the end, we used data from Sloan Digital Sky Survey to test our trained model on some real images.