# Deep Learning for Galaxy Mergers in the Galaxy Main Sequence

## TL;DR

This study uses deep learning on SDSS images to classify galaxy mergers and analyze their position relative to the galaxy main sequence, revealing mergers are spread across the entire star formation rate-stellar mass plane.

## Contribution

It introduces a deep learning classification method for galaxy mergers and provides new insights into their distribution on the galaxy main sequence.

## Key findings

- Mergers are classified with 91.5% accuracy.
- Merging galaxies are distributed across the entire main sequence.
- Deep learning effectively identifies galaxy mergers in large surveys.

## Abstract

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim. Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 91.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.