用深度学习从欧几里得数据中高效找出强引力透镜，发现130个新候选体。
Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification
- 构建端到端流水线，结合图像筛选与增强，生成高质量透镜候选图块。
- 三轮迭代后识别出441个高置信度透镜，其中130个为新增发现。
- 方法可扩展至未来数据发布，适合天文图像自动分析研究者。
我们提出一种端到端、迭代式流水线，用于高效识别强星系-星系引力透镜系统，应用于欧几里得望远镜第一阶段（Q1）成像数据。基于VIS星表，剔除点源，对透镜星系施加亮度限制（I_E ≤ 24 AB mag），并进行像素级伪影/噪声过滤，生成96×96像素的裁剪图像；通过以VIS为基准的亮度方案构建VIS+NISP彩色合成图，保留VIS形态与NISP色对比。使用仅含VIS的种子分类器获取明确正例与典型假阳性，从中构建形态平衡的负样本集，并增强稀有正例。初始六种CNN中，改进的VGG16（GlobalAveragePooling + 256/128全连接层，最后九层可训练）表现最佳。训练集由27个种子透镜（扩增至1809）和2000个负样本，扩展至30,686张彩色图像。经过三轮迭代微调，最终模型对前4000名候选者人工评分，获得441个A/B级候选透镜系统，其中311个与现有Q1透镜目录重合，130个为新发现（9个A类，121个B类）。独立验证显示，模型在前20,000预测中恢复了905个候选透镜中的740个（81.8%），包括偏心样本。候选体范围为I_E ≈ 17–24 AB mag（中位数21.3 AB mag），Y_E–H_E颜色更红，符合大质量早期型透镜星系特征。每轮训练需小团队一周时间，该方法易于扩展至未来欧几里得释放数据；后续工作将通过透镜注入校准选择函数，探索不确定性感知主动学习提升召回率，并尝试多尺度或注意力机制网络，配合快速后处理验证器整合透镜模型。
原文摘要 · Abstract (English)
We present an end-to-end, iterative pipeline for efficient identification of strong galaxy--galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from VIS catalogues, we reject point sources, apply a magnitude cut (I$_E$ $\leq$ 24) on deflectors, and run a pixel-level artefact/noise filter to build 96 $\times$ 96 pix cutouts; VIS+NISP colour composites are constructed with a VIS-anchored luminance scheme that preserves VIS morphology and NISP colour contrast. A VIS-only seed classifier supplies clear positives and typical impostors, from which we curate a morphology-balanced negative set and augment scarce positives. Among the six CNNs studied initially, a modified VGG16 (GlobalAveragePooling + 256/128 dense layers with the last nine layers trainable) performs best; the training set grows from 27 seed lenses (augmented to 1809) plus 2000 negatives to a colour dataset of 30,686 images. After three rounds of iterative fine-tuning, human grading of the top 4000 candidates ranked by the final model yields 441 Grade A/B candidate lensing systems, including 311 overlapping with the existing Q1 strong-lens catalogue, and 130 additional A/B candidates (9 As and 121 Bs) not previously reported. Independently, the model recovers 740 out of 905 (81.8%) candidate Q1 lenses within its top 20,000 predictions, considering off-centred samples. Candidates span I$_E$ $\simeq$ 17--24 AB mag (median 21.3 AB mag) and are redder in Y$_E$--H$_E$ than the parent population, consistent with massive early-type deflectors. Each training iteration required a week for a small team, and the approach easily scales to future Euclid releases; future work will calibrate the selection function via lens injection, extend recall through uncertainty-aware active learning, explore multi-scale or attention-based neural networks with fast post-hoc vetters that incorporate lens models into the classification.
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