Identifying post-starburst galaxies from surveys

Identifying such galaxies within the planned survey footprint of the Large Synoptic Survey Telescope (LSST) before a transient is detected will make TDE classification immediate and follow-up more efficient. Unfortunately, spectra for identifying most such galaxies are unavailable, and simple photometric selection is ineffective; cutting on "green valley" UV/optical/IR colors produces samples that are highly contaminated and incomplete.

Here we propose a new strategy using only photometric optical/UV/IR data from large surveys. Applying a machine-learning random forest classifier to a sample of ~400000 SDSS galaxies with Galaxy Evolution Explorer (GALEX) and Wide-field Infrared Survey Explorer (WISE) photometry, including 13592 quiescent Balmer-strong galaxies, we achieve 53%-61% purity and 8%-21% completeness, given the range in redshift. For the subset of 1299 post-starburst galaxies, we achieve 63%-73% purity and 5%-12% completeness.