<i>Euclid</i> : Finding strong gravitational lenses in the early release observations using convolutional neural networks
Bibliographic record
Abstract
Several new galaxy-galaxy strong gravitational lenses have been detected in the early release observations (ERO) from Euclid . The all-sky survey is expected to find 170 000 new systems, which are expected to greatly enhancing studies of dark matter and dark energy, and to constrain the cosmological parameters better. As a first step, we visually inspect all galaxies in one of the ERO fields (Perseus) to identify candidate strong-lensing systems and compared them to the predictions from convolutional neural networks (CNNs). The entire ERO dataset is too large for an expert visual inspection, however. In this paper, we therefore extend the CNN analysis to the whole ERO dataset and use different CNN architectures and methods. Using five CNN architectures, we identified 8469 strong gravitational lens candidates from I E -band cutouts of 13 Euclid ERO fields and narrowed them down to 97 through visual inspection. The sample includes 14 grade A and 31 grade B candidates. We present the spectroscopic confirmation of a strong gravitational lensing candidate, EUCL J081705.61+702348.8. The foreground lensing galaxy, an early-type system at z = 0.335, and the background source, a star-forming galaxy at z = 1.475 with [O II ] emission, are both identified. The lens modelling with the Euclid strong lens modelling pipeline revealed two distinct arcs in a lensing configuration, with an Einstein radius of 1.″18 ± 0.″03. This confirms the lensing nature of the system. These findings demonstrate that CNN-based candidate selection followed by visual inspection provides an effective approach for identifying strong lenses in Euclid data. They also highlight areas for improvement in future large-scale implementations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".