Quasars experiencing strong lensing offer unique viewpoints on subjects like
the cosmic expansion rate, the dark matter profile within the foreground
deflectors, and the quasar host galaxies. Unfortunately, identifying them in
astronomical images is challenging since they are overwhelmed by the abundance
of non-lenses. To address this, we have developed a novel approach by
ensembling cutting-edge convolutional networks (CNNs) -- i.e., ResNet,
Inception, NASNet, MobileNet, EfficientNet, and RegNet -- along with vision
transformers (ViTs) trained on realistic galaxy-quasar lens simulations based
on the Hyper Suprime-Cam (HSC) multiband images. While the individual model
exhibits remarkable performance when evaluated against the test dataset,
achieving an area under the receiver operating characteristic curve of $>$97.4%
and a median false positive rate of 3.1%, it struggles to generalize in real
data, indicated by numerous spurious sources picked by each classifier. A
significant improvement is achieved by averaging these CNNs and ViTs, resulting
in the impurities being downsized by factors up to 40. Subsequently, combining
the HSC images with the UKIRT, VISTA, and unWISE data, we retrieve
approximately 60 million sources as parent samples and reduce this to 892,609
after employing a photometry preselection to discover $z>1.5$ lensed quasars
with Einstein radii of $\theta_\mathrm{E}<5$ arcsec. Afterward, the ensemble
classifier indicates 3991 sources with a high probability of being lenses, for
which we visually inspect, yielding 161 prevailing candidates awaiting
spectroscopic confirmation. These outcomes suggest that automated deep learning
pipelines hold great potential in effectively detecting strong lenses in vast
datasets with minimal manual visual inspection involved.

本研究使用卷积神经网络和深度学习管道自动检测强透镜效应，并在多波段图像中检测到了可以用于光谱学确认的 161 个候选源。

通过组合神经网络简化多波段映射中的透镜状类星体鉴定

Streamlined Lensed Quasar Identification in Multiband Images via  Ensemble Networks

Strong lensing at the galaxy scale is useful for numerous applications in
Astrophysics and Cosmology. Some of the principal applications are studying the
mass structure, formation, and evolution of elliptical galaxies, constraining
the stellar initial mass function, and measuring cosmological parameters. Since
the first discovery of a galaxy-scale strong lens in the eighties, this field
has come a long way regarding data quality and techniques to model the data. In
this review article, we describe the most common methodologies to model lensing
observables of galaxy-scale strong lenses, especially the imaging data, as it
is the most available and informative source of lensing observables. We review
the main results from the literature in astrophysical and cosmological
applications of galaxy-scale strong lenses. We also discuss the current
limitations of the data and methodologies and provide a future outlook of the
expected development and improvements in both aspects in the near future.

本文综述了大规模强引力透镜成像数据的建模技术与应用，包括研究椭圆星系的质量结构、形成和演化，限制恒星初始质量函数，以及测量宇宙学参数等方面，并讨论了当前数据和方法的局限性以及未来的发展前景。

星系的强引力透镜效应

Strong Lensing by Galaxies

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。

利用卷积神经网络在暗能量调查中搜索强透镜系统

Strong lens systems search in the Dark Energy Survey using Convolutional  Neural Networks

We present a strong lensing modeling technique based on versatile basis sets
for the lens and source planes. Our method uses high performance Monte Carlo
algorithms, allows for an adaptive build up of complexity and bridges the gap
between parametric and pixel based reconstruction methods. We apply our method
to a HST image of the strong lens system RXJ1131-1231 and show that our method
finds a reliable solution and is able to detect substructure in the lens and
source planes simultaneously. Using mock data we show that our method is
sensitive to sub-clumps with masses four orders of magnitude smaller than the
main lens, which corresponds to about $10^8 M_{\odot}$, without prior knowledge
on the position and mass of the sub-clump. The modelling approach is flexible
and maximises automation to facilitate the analysis of the large number of
strong lensing systems expected in upcoming wide field surveys. The resulting
search for dark sub-clumps in these systems, without mass-to-light priors,
offers promise for probing physics beyond the standard model in the dark matter
sector.

本文提出基于万能基组的强透镜建模方法，使用高性能蒙特卡罗算法，桥接参数化与像素化重建方法的差距，可发现镜中和源面的亚结构，跨度范围宽，为大规模强透镜天体观测提供了新方法。