New lens candidates from GaSNets

To prepare for this challenge, we introduce a family of deep learning tools to classify features in one-dimensional spectra. As the first application of these Galaxy Spectra neural Networks (GaSNets), we focus on tools specialized in identifying emission lines from strongly lensed star-forming galaxies in the eBOSS spectra.

We first discuss the training and testing of these networks and define a threshold probability, PL, of 95% for the high-quality event detection. Then, using a previous set of spectroscopically selected strong lenses from eBOSS, confirmed with the Hubble Space Telescope (HST), we estimate a completeness of ~80% as the fraction of lenses recovered above the adopted PL. We finally apply the GaSNets to ~1.3M eBOSS spectra to collect the first list of ~430 new high-quality candidates identified with deep learning from spectroscopy and visually graded as highly probable real events.