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The most useful gravitational-lens dataset combines wide sky coverage, enough angular resolution and depth to reveal arcs or multiple images, documented image quality and masks, searchable source catalogs, and redshifts or other follow-up data. For most projects, start with a broad imaging survey, then add spectroscopy and carefully documented labels for validation and training.
Start by defining the lensing problem
A dataset is useful only in relation to the question you want to answer. This guide focuses on galaxy-scale strong lenses: a foreground galaxy bends light from a more distant object into arcs, rings, or multiple images. Finding these systems is different from measuring weak lensing statistically. Weak-lensing work generally needs large, calibrated shape catalogs and carefully modeled shear systematics; a strong-lens search needs images in which distinctive, often tiny structures can be recognized.
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For discovery, the priority is a large imaging footprint. For confirmation, you need evidence that the apparent foreground lens and background source really lie at different distances, usually through spectroscopy or reliable redshift estimates. For machine-learning triage, morphology labels and candidate lists are valuable, but a volunteer classification, model score, or candidate flag is not automatically a confirmed lens.
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1. Footprint and survey overlap
Strong lenses are rare, so a larger usable footprint generally provides more chances to find foreground galaxies aligned with distant sources. Area alone is not a quality score: compare the exact release footprint, target selection, depth, and usable coverage. Overlap with another imaging survey or a spectroscopy program is particularly useful because it enables cross-matching and follow-up.
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2. Resolution, seeing, and depth
Arcs may be smaller than a typical galaxy and much fainter than the foreground object. Check the pixel scale, point-spread function or seeing, limiting depth, exposure variation, and whether images are single exposures or coadds. A deep image with poor seeing can hide a narrow arc; a sharp image that is too shallow can miss the background source entirely. The relevant question is not whether a survey is “deep” in general, but whether its data can separate the expected lensing structure from the lens galaxy and noise.
3. Multiple bands and wavelength coverage
Color information helps distinguish a foreground lens from a differently colored background source and can expose artifacts that appear in only one band. Optical bands are often the starting point for morphology. Infrared measurements can add information about dusty or red sources, but their pixel scale, sensitivity, and coverage may differ from the optical data. Treat each band as a documented measurement, not as interchangeable imaging.
4. Image-quality metadata and masks
A nominal footprint includes regions that may be unusable. Before searching, inspect weight maps, bad-pixel masks, bright-star masks, satellite trails, detector gaps, edge effects, and variations in seeing or sky background. A candidate near a masked region or a bright-star artifact needs a different level of scrutiny from one in clean, uniform coverage. Record the quality flags used to exclude or prioritize objects so that a later completeness estimate is meaningful.
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5. Catalogs that lead back to pixels
A useful source catalog should provide stable object identifiers, sky positions, photometry, uncertainty or quality flags, and links to images or cutouts. Morphology and profile measurements can help rank targets, but catalogs do not replace pixel inspection: an arc can be blended into the lens galaxy or omitted by a deblender. Confirm that the catalog and image release are matched, and retain the release identifier in your project metadata.
6. Redshifts and ancillary observations
A foreground galaxy and a lensed source should normally have different distances. Spectroscopic redshifts provide the strongest routine check and support lens modeling and physical interpretation. Photometric redshifts can prioritize candidates when spectra are unavailable, but they carry uncertainties and catastrophic-outlier risks. A dataset that contains only imaging may be excellent for discovery while being insufficient for confirmation.
7. Labels with provenance
Document whether each label comes from a simulation, a machine-learning prediction, a volunteer vote, an expert review, a lens model, or a spectroscopic confirmation. Store confidence, vote fractions, grades, and the criteria used. Training on a mixed file of confirmed lenses, unvetted candidates, and negative examples without provenance can produce a model that learns survey artifacts or annotator habits instead of lens morphology.
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8. Access, scale, and reproducibility
Check the file format, query service, cutout interface, data volume, rate limits, storage needs, license, and citation requirements before committing to a workflow. A survey can be scientifically suitable but operationally impractical if you cannot retrieve consistent cutouts or reproduce the catalog version later.
Choose the resource family that matches your task
| Task | Most useful data | What it can establish | What it cannot establish alone |
|---|---|---|---|
| Broad candidate discovery | Wide-field, multiband imaging with source catalogs and cutouts | Whether a system has visual features compatible with strong lensing | That the alignment is physical or that the candidate is complete and pure |
| Foreground/background validation | Spectra or well-characterized redshift data matched to imaging | Distance ordering and measurements needed for interpretation | That every imaged arc is a genuine lensed source |
| Training and triage | Images plus labels whose origin, confidence, and selection are recorded | Supervised examples and ranking signals | Unbiased labels if the sample is selected or model-generated |
| Population studies | Imaging, redshifts, selection functions, masks, and follow-up completeness information | Rates or distributions after modeling selection effects | A simple count that can be compared across surveys without calibration |
What the major public resources provide
DESI Legacy Imaging Surveys: a wide discovery foundation
The DR10 data-release description documents optical image stacks and source catalogs from the MzLS, DECaLS, and BASS programs. The original dedicated imaging covered about 14,000 square degrees in g, r, and z, supplemented by four infrared bands from NEOWISE. DR10 also added public DECam imaging and extended the inference model over more than 20,000 square degrees. The documentation lists coverage and pass-count differences by band, so do not assume uniform depth across the footprint.
The Legacy Surveys homepage identifies DR11 as the current release and describes catalogs covering roughly 31,000 square degrees of extragalactic sky in optical and infrared bands. Release status and contents can change; use the release-specific documentation for the images, catalog schema, and citation attached to your analysis. This combination of broad imaging, searchable catalogs, and online viewing or raw-data routes makes the Legacy Surveys a practical starting point for candidate discovery, not a substitute for confirmation data.
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DESI spectroscopy: redshift validation
The DESI DR1 Strong Lensing Catalog documents spectroscopic observations of strong-lens targets. Its broader observing program seeks redshifts for both lensing galaxies and lensed sources for approximately 1,800 new candidate systems. The page states that the downloadable DR1 content is only the observations available in DR1 and is a subset of the DR2 sample; the program figure must not be treated as the row count of the DR1 file.
The documented product is desi-sl-vac-v1.fits. Its fields include target identifiers, survey and program labels, sky-pixel identifiers, redshift, and redshift error. Read the data model and release scope before merging it with imaging, and preserve the redshift uncertainties rather than treating every value as exact.
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Galaxy Zoo: morphology and training resources
The Galaxy Zoo data portal provides several kinds of morphology information. Galaxy Zoo DESI contains model-generated measurements for 8.67 million galaxies from Legacy Imaging Surveys data. The portal reports that model predictions typically reproduce volunteer answer fractions within 5–10% for each answer and provides friendly and advanced catalogs, cross-match services, code, and model weights. The prepared model images are not bundled in that release; the portal directs users to the Legacy Survey cutout service for original pixels.
Galaxy Zoo Euclid resources include volunteer-facing images, morphology measurements, physical properties from Euclid MER catalogs, and learned image representations. Galaxy Zoo: Cosmic Dawn classifications cover more than 41,000 galaxies across 6 square degrees of Euclid Deep Field North. The associated HSC imaging reaches apparent magnitude 21.5 in HSC-i, with photometric redshifts to about 2.5 where available. That release reports 51 new gravitational lenses in its defined field and supplies classifications, model predictions, images, and subject metadata.
These resources are useful for creating training sets, checking morphology, and selecting follow-up targets. They are not interchangeable with a spectroscopically confirmed lens catalog: volunteer labels and model outputs inherit the selection and uncertainty of the process that produced them.
Euclid’s strong-lens candidate release
In an ESA announcement dated March 2025, Euclid presented a first catalog of 500 galaxy-galaxy strong-lens candidates. The workflow combined AI screening, citizen-science inspection, expert vetting, and modeling. It was a selected-area quick data release, not a complete Euclid survey catalog. ESA reported that about 2,000 square degrees had been observed by 19 March 2025 out of a planned 14,000-square-degree survey.
The same announcement projected approximately 7,000 candidates in a major data release planned for the end of 2026 and around 100,000 galaxy-galaxy strong lenses by mission end. Those are dated projections, not delivered totals. The release illustrates why combining automated screening, human inspection, expert review, and modeling can scale discovery while still requiring explicit candidate status.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical workflow for selecting and using a dataset
- State the target population. Specify galaxy-scale strong lenses, expected source brightness and redshift range, and whether you need discovery, confirmation, or population measurements. Exclude weak-lensing shape catalogs if your goal is visual strong-lens candidates.
- Define the minimum image requirements. Set acceptable seeing or point-spread function, pixel scale, depth, bands, and signal-to-noise for the arcs or multiple images you expect to find.
- Map the usable footprint. Download or query coverage, masks, depth and seeing maps, and band-by-band pass information. Quantify the area that remains after quality cuts rather than quoting the survey’s headline area.
- Inspect the catalog schema. Verify identifiers, coordinates, photometric uncertainties, morphology fields, flags, and a reproducible route from each row to an image cutout.
- Build a candidate view. Use catalog measurements or morphology scores to prioritize objects, then inspect multiband pixels. Keep rejected artifacts and ambiguous objects with reason codes so that the selection process is auditable.
- Attach redshift information. Cross-match spectra first where available; add photometric estimates with their uncertainties and quality flags. Treat a missing source redshift as an unresolved validation step, not as evidence that the candidate is false.
- Separate label classes. Maintain distinct fields for simulated examples, volunteer classifications, model predictions, expert-vetted candidates, and confirmed systems. Do not collapse them into one binary truth column.
- Plan validation and follow-up. Reserve a test sample that was not used for training, measure performance by image quality and redshift regime, and seek spectroscopy or deeper imaging for high-value candidates.
- Record the release. Save the survey release, catalog version, query date, filters, masks, and code environment. The same named survey can change substantially between releases.
Common mistakes and how to avoid them
- Equating area with discovery power: a large but shallow or poorly resolved footprint may miss the systems a smaller, sharper survey reveals.
- Calling candidates confirmed lenses: retain the word “candidate” until the cited evidence establishes confirmation.
- Mixing release statistics: the 2021 DESI announcement of 1,210 DR8 candidates describes one historical machine-learning search, not a current total or a survey-wide comparison. The DESI announcement should be cited with that date and release.
- Assuming uniform coverage: band coverage, number of passes, seeing, and masks vary across imaging surveys.
- Training on biased labels: model predictions and volunteer votes can be excellent triage signals while still reflecting the sample used to generate them.
- Using photometric redshifts as proof: they are useful prioritization data, but their uncertainty must be propagated and checked against spectroscopy when the science requires it.
- Ignoring artifacts: diffraction spikes, trails, detector edges, and deblending failures can imitate arcs. Require candidates to survive mask and multiband checks.
- Comparing counts without context: survey area, depth, target selection, date, candidate definition, and confirmation standards all change the meaning of a published number.
A defensible minimum data stack
For a first strong-lens search, use a wide-field optical imaging release with cutouts and catalogs, the associated masks and quality maps, and at least one complementary band where available. Add a redshift table for lens and source components, even if the first pass uses photometric estimates. Keep a separately labeled set of confirmed or expert-vetted systems for validation, and preserve rejected artifacts for testing false-positive control. This arrangement supports discovery without pretending that imaging alone supplies complete lens truth.
There is no universal winning survey. The right choice is the release whose resolution, depth, footprint, masks, catalogs, redshifts, labels, and access match your scientific target and whose selection limitations you can document. Use broad imaging to find candidates, spectroscopy or reliable redshifts to test the geometry, and provenance-rich labels to train and evaluate models.
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