Euclid Q1 strong gravitational lens

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 20000 predictions, considering off-centred samples.

Candidates span IE~17-24 AB mag (median 21.3 AB mag) and are redder in YE-HE than the parent population, consistent with massive early-type deflectors. Each training iteration required about a week for a small team, and the approach easily scales to future wide-area Euclid releases; future work will calibrate the selection function via lens injection, extend recall through uncertainty-aware active learning, and explore multi-scale or attention-based neural networks with fast post-hoc vetters that incorporate lens models into the classification.