Deep learning for galaxy mergers in the galaxy main sequence

William J. Pearson*, Lingyu Wang, James Trayford, Carlo E. Petrillo, Floris F.S. Van Der Tak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)104-108
Number of pages5
JournalProceedings of the International Astronomical Union
Issue numberS341
Publication statusPublished - 1 Nov 2019


  • galaxies: evolution
  • galaxies: interactions
  • galaxies: starburst
  • infrared: galaxies
  • methods: data analysis


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