用物理约束的生成模型，毫秒级识别引力透镜类星体并估计其参数。
Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders
- 基于物理先验的变分自编码器，融合图像重建、分类与透镜建模。
- 对20个已知透镜系统验证，参数估计在3角秒内与传统方法一致。
- 从8000万源中筛选出1.3万高概率候选，42个新目标待光谱确认。
强引力透镜类星体为宇宙膨胀速率、前景暗物质分布及类星体宿主特性提供了重要线索，但其在天文图像中的检测因非透镜天体干扰而困难。为此，我们开发了基于物理信息变分自编码器的生成模型VariLens，集成图像重建、物体分类与透镜建模三大模块，实现强透镜分析的快速全面处理。VariLens可在单个CPU上仅用毫秒时间，同时判断对象是否为透镜系统，并估计奇异等温椭球（SIE）质量模型的关键参数——爱因斯坦半径（θₐ）、透镜中心与扁率。与传统建模方法在20个已知透镜系统上的对比显示，结果在θₐ < 3弧秒的系统中均在2σ范围内一致。为发现新候选，我们以约8000万源为基础，结合斯巴鲁超广角相机（HSC）数据与多波段巡天信息，经光度预筛选（目标z > 1.5）后剩71万源，再由VariLens识别出13,831个高概率透镜候选。人工视觉评估后获得42个潜在新候选，等待光谱确认。该结果凸显自动化深度学习流水线在大规模数据中高效探测与建模强透镜的巨大潜力。
原文摘要 · Abstract (English)
Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we developed a generative deep learning model called VariLens, built upon a physics-informed variational autoencoder. This model seamlessly integrates three essential modules: image reconstruction, object classification, and lens modeling, offering a fast and comprehensive approach to strong lens analysis. VariLens is capable of rapidly determining both (1) the probability that an object is a lens system and (2) key parameters of a singular isothermal ellipsoid (SIE) mass model -- including the Einstein radius ($θ_\mathrm{E}$), lens center, and ellipticity -- in just milliseconds using a single CPU. A direct comparison of VariLens estimates with traditional lens modeling for 20 known lensed quasars within the Subaru Hyper Suprime-Cam (HSC) footprint shows good agreement, with both results consistent within $2σ$ for systems with $θ_\mathrm{E}<3$ arcsecs. To identify new lensed quasar candidates, we begin with an initial sample of approximately 80 million sources, combining HSC data with multiwavelength information from various surveys. After applying a photometric preselection aimed at locating $z>1.5$ sources, the number of candidates was reduced to 710,966. Subsequently, VariLens highlights 13,831 sources, each showing a high likelihood of being a lens. A visual assessment of these objects results in 42 promising candidates that await spectroscopic confirmation. These results underscore the potential of automated deep learning pipelines to efficiently detect and model strong lenses in large datasets.
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