Using deep Residual Networks to search for galaxy-Ly{\alpha} emitter
  lens candidates based on spectroscopic-selection
  More than one hundred galaxy-scale strong gravitational lens systems have
been found by searching for the emission lines coming from galaxies with
redshifts higher than the lens galaxies. Based on this spectroscopic-selection
method, we introduce the deep Residual Networks (ResNet, a kind of deep
Convolutional Neural Networks) to search for the galaxy-Ly$\alpha$ emitter
(LAE) lens candidates by recognizing the Ly$\alpha$ emission lines coming from
high redshift galaxies ($2 < z < 3$) in the spectra of early-type galaxies
(ETGs) at middle redshift ($z\sim 0.5$). The spectra of the ETGs come from the
Data Release 12 (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS) of
the Sloan Digital Sky Survey \uppercase\expandafter{\romannumeral3}
(SDSS-\uppercase\expandafter{\romannumeral3}). In this paper, we first build a
28 layers ResNet model, and then artificially synthesize 150,000 training
spectra, including 140,000 spectra without Ly$\alpha$ lines and 10,000 ones
with Ly$\alpha$ lines, to train the networks. After 20 training epochs, we
obtain a near-perfect test accuracy at 0.9954. The corresponding loss is 0.0028
and the completeness is 93.6\%. We finally apply our ResNet model to our
predictive data with 174 known lens candidates. We obtain 1232 hits including
161 of the 174 known candidates (92.5\% discovery rate). Apart from the hits
found in other works, our ResNet model also find 536 new hits. We then perform
several subsequent selections on these 536 hits and present 5 most believable
lens candidates.
