Deep learning for galaxy mergers in the galaxy main sequence

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 im...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the International Astronomical Union 2019-11, Vol.15 (S341), p.104-108
Main Authors: Pearson, William J., Wang, Lingyu, Trayford, James, Petrillo, Carlo E., van der Tak, Floris F. S.
Format: Article
Language:English
Subjects:
Astronomy
Contributed Papers
Deep learning
Digital imaging
Galaxy mergers & collisions
Machine learning
Sky surveys (astronomy)
Star & galaxy formation
Star formation rate
Starburst galaxies
Stellar mass
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 92.5%. 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.
ISSN:1743-9213
1743-9221
DOI:10.1017/S1743921319002187