We perform a semi-automated search for strong gravitational lensing systems
in the 9,000 deg$^2$ Dark Energy Camera Legacy Survey (DECaLS), part of the
DESI Legacy Imaging Surveys (Dey et al.). The combination of the depth and
breadth of these surveys are unparalleled at this time, making them
particularly suitable for discovering new strong gravitational lensing systems.
We adopt the deep residual neural network architecture (He et al.) developed by
Lanusse et al. for the purpose of finding strong lenses in photometric surveys.
We compile a training set that consists of known lensing systems in the Legacy
Surveys and DES as well as non-lenses in the footprint of DECaLS. In this paper
we show the results of applying our trained neural network to the cutout images
centered on galaxies typed as ellipticals (Lang et al.) in DECaLS. The images
that receive the highest scores (probabilities) are visually inspected and
ranked. Here we present 335 candidate strong lensing systems, identified for
the first time.

本文使用神经网络在 DECaLS 图像中鉴定出 335 个强引力透镜系统作为候选物体。