​
Galaxy mergers impact the evolution of galaxies by contributing to their mass growth and change in morphology thus motivating us to study them. Observational images capture mergers at a single instant in time making it hard to interpret their properties. Hence, we must resort to indirect means of assessing them by comparison with simulations. Simulations provide an all round quantitative understanding of galaxy mergers, their properties and impact on evolution. The idea is to utilize simulation data to infer observed galaxy merger properties. In this thesis, we train a Deep Neural Network model on galaxy merger images generated from EAGLE simulations with their corresponding properties namely size and mass ratio. We successfully generate two image sets of data for galaxy mergers, at z=0 and 20>z>0 separately, using two zooming techniques namely the EAGLE package and a self written zooming algorithm. The training results in an accuracy of 85% and 80% on the datasets z=0 and 20>z>0 for mass ratio and 90% and 70% for size ratio respectively. Considering that similar accuracies are achieved, we imply that the visualization techniques aren’t crucial to the training, suggesting that the model is robust. We also imply from the high accuracies achieved that Deep Neural Networks are an effective tool in studying galaxy mergers. Further, we conclude that with higher accuracies achieved by increasing the resolution of the images, this technique can be used to study observational images of galaxy mergers.
Using Deep Learning to predict the properties of galaxy major mergers in EAGLE simulations. ​
​
​
The relation between galaxy morphology and merger history in EAGLE simulations.
​
​
We investigate the link between merger history of galaxies, stellar mass and galaxy morphology using the EAGLE cosmological hydrodynamical simulations. We define the stellar mass of a galaxy as the total stellar mass within 30kpc, quantify galaxy morphology using a kinematic based parameter kappa-corotating and map the distribution of ellipticals and disks across redshifts in the simulation. We consider galaxy pairs within a certain spatial proximity to be impending merger events and differentiate them as major or minor depending on the ratio of the masses of the galaxies comprising it. The contribution of major mergers in the evolution of galaxies is estimated by the fraction of galaxies in close pairs, namely the fraction of major mergers (fMM). fMM re- veals that the probability of major mergers is independent of stellar mass but depends on morphology, with fMM being higher for ellipticals than disks at all redshifts, agreeing with recent observational estimates. We also find that independent of morphology, the probability of mergers increases with the increase in redshift. To understand the morphological composition of galaxies in close pairs, we calculate the fraction of the pairs constituting galaxies belonging to the same morphology. We find that the probability of finding two elliptical galaxies merging is higher than finding two disk type galaxies.
Predicting the Impact of Perturbers on Planetary Systems in Star Clusters
- Brendon Walter, Malavika Vasist, Lisa Dombrovsky, Vaibhav Vaidya, Zhongyue Zhang, Maxwell Cai, Leiden University
​