Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning
Bibliographic record
Abstract
Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0 . ″ 16 resolution covering a third of the sky, the Euclid telescope will revolutionise the identification of strong lenses, with 170 000 lenses forecasted to be discovered amongst the 1.5 billion galaxies it will observe. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of Euclid data (Q1) covering 63 deg 2 . The models have been validated by citizen scientists and expert visual inspection. We focus on the best-performing network: a fine-tuned version of the Zoobot pretrained model originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that Zoobot was tasked to find strong lenses within, the top 1000 ranked objects contain 122 grade A lenses (almost-certain lenses) and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection yielded 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top 20 000 by Zoobot . When extrapolated to the full Euclid survey, the highest ranked one million images will contain 75 000 grade A or B strong gravitational lenses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".