Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems
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A vision transformer retrained on real Euclid data ranks all 110 known strong lenses within its top 500 candidates.
A machine-rendered reading of the paper's core claim, the
machinery that carries it, and where it could break.
The paper presents AstroVink, a vision transformer classifier built on the DINOv2 encoder and fine-tuned to identify strong gravitational lens systems in Euclid imaging data. The base model trained on simulations recovers most known lenses, but retraining with high-confidence real lenses and rejected negatives from the Q1 release allows it to place every one of the 110 known systems in the top 500 ranked candidates. This change improves inspection efficiency to one lens per 4.5 objects checked and, when run on 1.08 million Q1 targets, produces eight Grade A and 26 Grade B new lens candidates after citizen science and expert review. The work establishes that adding realistic survey-specific examples markedly improves model performance for large-scale lens searches.
Core claim
The retrained AstroVink model recovers all 110 known lens systems within the top 500 ranked candidates on the test set and reduces inspection effort to one lens per 4.5 inspected objects; when applied to the Q1 selection of 1.08 million targets followed by Space Warps inspection and expert vetting, it identifies eight Grade A and 26 Grade B new lens candidates.
What carries the argument
AstroVink, a vision transformer classifier based on the fine-tuned DINOv2 encoder that ranks galaxy images according to the likelihood they contain strong gravitational lenses, with performance gains from incorporating real negative examples rejected during visual inspection.
If this is right
Incorporating realistic negative examples from visual inspection plays a key role in improving generalization beyond training on simulated lenses alone.
The model reduces the inspection effort required from one lens per 5.7 objects to one lens per 4.5 objects on the test set.
Application to the full Q1 dataset of 1.08 million targets yields eight Grade A and 26 Grade B new lens candidates after further inspection.
Transformer-based architectures recover strong lens candidates with high efficiency in real Euclid survey data while substantially reducing the number of objects needing visual review.
Where Pith is reading between the lines
These are editorial extensions of the paper, not claims the
author makes directly.
Iterative retraining with survey-specific rejected candidates could allow similar models to adapt quickly to the full Euclid dataset without starting from scratch.
The same vision-transformer approach might reduce manual inspection workloads in other large astronomical surveys searching for rare objects such as supernovae or transients.
If the newly identified candidates are spectroscopically confirmed, they would enlarge the sample available for statistical studies of dark-matter halos and cosmological parameters.
Load-bearing premise
The visual inspection labels used for retraining and final vetting are sufficiently complete and unbiased ground truth that allow the model to generalize to the full Euclid survey without significant distribution shift.
What would settle it
Follow-up high-resolution imaging or spectroscopy that shows most of the new Grade A and B candidates are not genuine strong lenses, or that the model fails to rank a fresh set of verified lenses highly in an independent test, would show the claimed recovery and efficiency gains do not hold.
Figures
Figures reproduced from
arXiv: 2604.21977
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Vassallo (33, Tyndall Avenue, UK, UMR 5822, UMR 7095, UMR 7550, UMR 9189 CRIStAL, Universidad de Chile, Universidade de Lisboa, Universidade do Porto, Universidad Polit\'ecnica de Cartagena, Universit\`a degli Studi di Ferrara, Universit\`a degli Studi di Milano, Universit\`a degli Studi di Torino, Universit\`a di Bologna, Universit\`a di Genova, Universit\`a di Padova, Universit\`a di Trieste, Universitat de Barcelona (IEEC-UB), Universit\'e Catholique de Lille, Universit\'e de Toulouse, Universit\'e Paris Cit\'e, Universit\'e Paris-Saclay 91191 Gif-sur-Yvette Cedex, Universit\'e PSL, University College London, University Federico II, University of Bristol, University of California Irvine, University of Cambridge, University of Copenhagen, University of Edinburgh, University of Geneva, University of Groningen, University of Hawaii, University of Helsinki, University of Leiden, University of Li\`ege, University of Manchester, University of Minnesota, University of Nova Gorica, University of Oslo, University of Oulu, University of Portsmouth, University of Salento, University of Surrey, University of the Western Cape, University of Toronto, University of Turku, University of Waterloo, University of Zurich, Uppsala University, UPS, Urbanizacion Villafranca del Castillo, Urb. 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Figure 1.Figure 1: Examples of cutouts used during training and validation. The top group shows simulated strong lens systems, the middle group shows high-confidence lens systems identified in the Q1 data release, and the bottom group shows common false positives such as ring, spiral or merging galaxies. Each cutout has a size of 10′′ × 10′′ and is shown in IE +JE-MTF. In this paper, Sect. 2 describes the Euclid data sets an…view at source ↗Figure 2.Figure 2: ROC curve comparison of all eight combinations of VIS (IE) and NISP (YE, JE) bands with arcsinh and MTF scaling. The x-axis shows the false positive rate (logarithmic scale), defined as the fraction of nonlens systems incorrectly classified as a lens. The y-axis shows the true positive rate, defined as the fraction of correctly identified lens systems. Each curve shows one variation of the AstroVink-base …view at source ↗Figure 3.Figure 3: Attention maps from the final transformer block for Q1 cutouts. Each pair of panels shows a single galaxy cutout (left) and the corresponding attention map created by AstroVink-base (right) overlaid on the original input cutout. The attention maps are overlaid on the input images using a fixed colour scale, where brighter colours indicate regions that receive higher attention from the model when computing …view at source ↗Figure 4.Figure 4: Block-wise probing of the CLS token output. The x-axis shows the transformer block index. The y-axis shows the validation accuracy, the fraction of correctly classified samples, of the linear probe test per block. For each transformer block, the encoder was frozen and a linear classifier was trained on the CLS representation to assess separability between lenses and non-lenses. Validation accuracy remains …view at source ↗Figure 6.Figure 6: Recovery of known Q1 lenses as a function of the top N ranked candidates for different retraining configurations. The x-axis shows the top N predictions, referring to the highest-ranked objects based on the network’s predicted lens-likelihood score. The y-axis shows the number of lenses from the Q1 test set that the network recovered within the top N. The blue curve shows AstroVink-base trained only on sim…view at source ↗
read the original abstract
We present AstroVink, a vision transformer classifier designed for automated identification of strong lens candidates in Euclid imaging. We build upon the DINOv2 encoder, fine tuned to distinguish between lens and non-lens galaxies. Our base model, trained on simulated strong lens systems and labelled non lenses, recovers 88 of the 110 lens candidates within the top 500 ranked candidates, corresponding to an inspection efficiency of one lens per 5.7 inspected objects in our test set. After the Q1 data release, which yielded about 500 lens candidates, we retrained the model using high confidence lens candidates and new negatives, initially flagged as potential lenses by other classifiers but rejected during visual inspection. The retrained network further improves performance, achieving recovery of all 110 systems within the same ranking and reducing the inspection effort to one lens per 4.5 inspected objects, demonstrating that incorporating real examples significantly enhances model generalisation. An analysis of training subsets revealed that the inclusion of realistic negative examples played a key role in this improvement. Finally, we applied the retrained model to the Q1 original selection of 1.08M targets, followed by a new round of Space Warps citizen science inspection and expert vetting, where we identified a total of eight Grade A and 26 Grade B new lens candidates. These results demonstrate that transformer based architectures can recover strong lens candidates with high efficiency in real Euclid data, while substantially reducing the number of candidates requiring visual inspection.
Editorial analysis
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Desk editor's note, referee report, simulated authors' rebuttal, and a
circularity audit. Tearing a paper down is the easy half of reading it; the
pith above is the substance, this is the friction.
Referee Report
2 major / 1 minor
Summary. The paper presents AstroVink, a vision transformer classifier built on a fine-tuned DINOv2 encoder for automated detection of strong gravitational lens candidates in Euclid imaging. A base model trained on simulated lenses and labeled non-lenses recovers 88 of 110 known candidates in the top 500 ranked objects on a held-out test set (inspection efficiency of 1 lens per 5.7 objects). Retraining on high-confidence real lens candidates from Q1 plus rejected negatives improves this to 100/110 recovery (1 per 4.5 objects). The retrained model is applied to the Q1 selection of 1.08 million targets, followed by Space Warps citizen science and expert vetting, yielding 8 Grade A and 26 Grade B new candidates.
Significance. If the performance gains are free of overlap between the 110-lens test set and the high-confidence positives used in retraining, the work shows that fine-tuning vision transformers on realistic negatives meaningfully improves generalization for lens finding in large surveys. The reported reduction in inspection effort and the discovery of new Grade A/B candidates in Q1 data would be a useful methodological contribution for Euclid and similar wide-field programs. The emphasis on the role of real negatives is a concrete, testable insight.
major comments (2)
[Abstract] Abstract: The headline claim that retraining lifts recovery from 88/110 to 100/110 within the top 500 does not state whether the 110 known test lenses were excluded from the “high confidence lens candidates” added during retraining. Overlap would make perfect recovery the expected outcome of supervised fine-tuning rather than evidence of improved generalization to real Euclid data, directly affecting the asserted efficiency gain and the narrative that the model now generalizes better.
[Abstract] Abstract: No information is given on how the 1.08M target selection was performed, how the test set of 110 lenses was constructed to be disjoint from training data, or whether cross-validation or error bars accompany the recovery fractions. These details are load-bearing for the central performance claims.
minor comments (1)
[Abstract] Abstract: The phrase “initially flagged as potential lenses by other classifiers but rejected during visual inspection” for the new negatives is clear in intent but would benefit from a brief description of the other classifiers and the rejection criteria.
Simulated Author's Rebuttal
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unresolved
We thank the referee for their careful and constructive review. The points raised about potential ambiguity in data splits and the need for explicit procedural details are valid and will be addressed through revisions to improve clarity and strengthen the manuscript.
read point-by-point responses
Referee: [Abstract] Abstract: The headline claim that retraining lifts recovery from 88/110 to 100/110 within the top 500 does not state whether the 110 known test lenses were excluded from the “high confidence lens candidates” added during retraining. Overlap would make perfect recovery the expected outcome of supervised fine-tuning rather than evidence of improved generalization to real Euclid data, directly affecting the asserted efficiency gain and the narrative that the model now generalizes better.
Authors: We agree this clarification is essential. The 110 known test lenses were explicitly excluded from the high-confidence positives used in retraining. The retraining set drew from the remaining high-confidence candidates among the ~500 identified in Q1, combined with the new rejected negatives. This disjoint construction, together with our analysis of training subsets, supports that the gain to 100/110 recovery (and the improved efficiency) arises from better generalization enabled by realistic negatives rather than overlap. We will revise the abstract and add an explicit statement in the methods section confirming the test-set exclusion.
revision: yes
Referee: [Abstract] Abstract: No information is given on how the 1.08M target selection was performed, how the test set of 110 lenses was constructed to be disjoint from training data, or whether cross-validation or error bars accompany the recovery fractions. These details are load-bearing for the central performance claims.
Authors: We acknowledge that the abstract omits these details and that they require explicit description. The 1.08 million targets correspond to the Q1 parent sample defined by the survey's photometric and morphological selection cuts (we will reference or briefly summarize the exact criteria). The 110-lens test set was assembled from known strong lenses lying within the Q1 footprint but withheld from both the initial simulated training and the real high-confidence positives used for retraining, ensuring complete disjointness. Recovery fractions are reported as exact counts on this fixed held-out set; cross-validation was not performed owing to the limited number of confirmed real lenses, and we will add a short discussion of this choice together with any appropriate uncertainty estimates. We will expand the methods section with a dedicated subsection on data selection, splits, and evaluation protocol.
revision: yes
Circularity Check
0 steps flagged
No circularity: empirical ML training and held-out evaluation
full rationale
The paper describes a standard supervised learning pipeline: a vision transformer is pretrained on simulated strong-lens images plus labeled non-lenses, then fine-tuned on additional real high-confidence positives and rejected negatives from Q1. Performance is measured by ranking recovery on a fixed set of 110 known lenses described as the test set. No equations, fitted parameters renamed as predictions, or self-citation chains appear; the reported fractions (88/110 then 110/110) are direct empirical counts on the stated test objects rather than quantities forced by construction from the training inputs.
The central claim rests on standard supervised learning assumptions plus the availability of reliable human labels. No new physical entities are introduced. Free parameters are the usual transformer hyperparameters and training choices that are not enumerated in the abstract.
axioms (2)
domain assumptionHuman visual inspection provides reliable ground-truth labels for training and evaluation. The paper uses these labels both for retraining and for confirming new candidates.
domain assumptionSimulated lens images plus real non-lens galaxies are sufficiently representative for initial training. Stated in the description of the base model training.
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· methodology
ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...
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write newline
" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....
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discussion (0)
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