Semantic Scholar Open Access 2026

Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification

Euclid Collaboration X. Xu R. Chen T. Li A. Cooray S. Schuldt +495 lainnya

Abstrak

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.

Topik & Kata Kunci

Penulis (500)

E

Euclid Collaboration X. Xu

R

R. Chen

T

T. Li

A

A. Cooray

S

S. Schuldt

J

J. A. A. Barroso

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D. Stern

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D. Scott

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G. Verza

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H. I. O. Astronomy

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U. C. Irvine

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Irvine CA 92697

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U. California

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3551 Trousdale Parkway

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CA 90089

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Dipartimento di FisicaAldo Pontremoli

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20133 Milano

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Inaf-Iasf Milano

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Format Sitasi

Xu, E.C.X., Chen, R., Li, T., Cooray, A., Schuldt, S., Barroso, J.A.A. et al. (2026). Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification. https://www.semanticscholar.org/paper/5915848c2b9029e6bb0c804133de327a1d33b405

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