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.