  With the advent of new spectroscopic surveys from ground and space, observing
up to hundreds of millions of galaxies, spectra classification will become
overwhelming for standard analysis techniques. 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 at 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 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
spectra to collect a first list of ~430 new high quality candidates identified
with deep learning applied to spectroscopy and visually graded as highly
probable real events. A preliminary check against ground-based observations
tentatively shows that this sample has a confirmation rate of 38%, in line with
previous samples selected with standard (no deep learning) classification tools
and follow-up by Hubble Space Telescope. This first test shows that machine
learning can be efficiently extended to feature recognition in the wavelength
space, which will be crucial for future surveys like 4MOST, DESI, Euclid, and
the Chinese Space Station Telescope (CSST).
