  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. To study different phenomena related to galaxy
interactions, various parameters like the mass ratio of the interacting galaxy,
orbital parameters, mass distribution, morphologies are required. Convolutional
Neural Networks (CNN) are widely used to classify image data. Thus, we used CNN
as our approach to the problem. In this work, we will be using 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 (ResNet).
The dynamical parameters we are aiming for; would be spin, relative inclination
($i$), viewing angle ($\theta$), and azimuthal angle ($\phi$). We aim to
download bulk data using the web scraping method. The first approach is to
create different combinations of these parameters to form 60 classes. Feeding
the data into the model, we achieved 93.63% accuracy. As we received good
results in minute classification, we moved to our second approach, regression.
Here the model can predict the continuous and exact values of the dynamical
parameters. We have achieved a 99.86% R-squared value and the mean squared
error of 0.0833 on testing data. In the end, we used data from Sloan Digital
Sky Survey to test our trained model on some real images.
