We performed a search for strong lens galaxy-scale systems in the first data
release of the Dark Energy Survey (DES), from a color-selected parent sample of
18~745~029 Luminous Red Galaxies (LRGs). Our search was based on a
Convolutional Neural Network (CNN) to grade our LRG selection with values
between 0 (non-lens) and 1 (lens). Our training set was data-driven, i.e. using
lensed sources taken from HST COSMOS images and where the light distribution of
the lens plane was taken directly from DES images of our LRGs. A total of
76~582 cutouts obtained a score above 0.9. These were visually inspected and
resulted in two catalogs. The first one contains 405 lens candidates, where 90
present clear lensing features and counterparts, while the others 315 require
more evidence, such as higher resolution images or spectra to be conclusive. A
total of 186 candidates were totally new identified in this search. The second
catalog includes 539 ring galaxy candidates that will be useful to train CNNs
against this type of false positives. For the 90 best lens candidates we
carried out color-based deblending of the lens and source light without fitting
any analytical profile to the data. The method turned out to be very efficient
in the deblending, even for very compact objects and for objects with very
complex morphology. Finally, from the 90 best lens candidates we selected 52
systems having one single deflector, to test an automated modeling pipeline
which successfully modeled 79\% of the sample within an acceptable amount of
computing time.

使用卷积神经网络从第一批 Dark Energy Survey（DES）数据中筛选出强引力透镜系统的候选物，包括 405 个透镜候选物和 539 个可用于训练 CNNs 的环状星系候选物，并构建自动化建模管道来对 52 个有单个偏转器的系统进行建模。