J/A+A/712/A207 Euclid Q1 strong gravitational lens (Euclid Coll., 2026)
Euclid Quick Data Release (Q1). XLI. AgileLens:
A scalable CNN-based pipeline for strong gravitational lens identification.
Euclid Coll., Xu X., Chen R., Li T., Cooray A.R., Schuldt S.,
Acevedo Barroso J.A., Stern D., Scott D., Meneghetti M., Despali G.,
Chopra J., Cao Y., Cheng M., Buda J., Zhang J., Furumizo J., Valencia R.,
Jiang Z., Tortora C., Lines N.E.P., Collett T.E., Fotopoulou S., Galan A.,
Manjon-Garcia A., Gavazzi R., Iwamoto L., Kruk S., Millon M., Nugent P.,
Saulder C., Sluse D., Wilde J., Walmsley M., Courbin F., Metcalf R.B.,
Altieri B., Amara A., Andreon S., Auricchio N., Baccigalupi C., Baldi M.,
Balestra A., Bardelli S., Battaglia P., Bender R., Biviano A., Branchini E.,
Brescia M., Camera S., Capobianco V., Carbone C., Cardone V.F.,
Carretero J., Casas S., Castellano M., Castignani G., Cavuoti S.,
Cimatti A., Colodro-Conde C., Congedo G., Conselice C.J., Conversi L.,
Copin Y., Courtois H.M., Cropper M., Da Silva A., Degaudenzi H.,
De Lucia G., Dolding C., Dole H., Dubath F., Dupac X., Dusini S.,
Escoffier S., Farina M., Farinelli R., Farrens S., Ferriol S., Finelli F.,
Fosalba P., Frailis M., Franceschi E., Fumana M., Galeotta S., George K.,
Gillard W., Gillis B., Giocoli C., Gomez-Alvarez P., Gracia-Carpio J.,
Grazian A., Grupp F., Haugan S.V.H., Holmes W., Hormuth F., Hornstrup A.,
Jahnke K., Jhabvala M., Joachimi B., Kermiche S., Kiessling A., Kubik B.,
Kuemmel M., Kunz M., Kurki-Suonio H., Le Brun A.M.C., Ligori S.,
Lilje P.B., Lindholm V., Lloro I., Mainetti G., Maiorano E., Mansutti O.,
Marcin S., Marggraf O., Martinelli M., Martinet N., Marulli F., Massey R.J.,
Medinaceli E., Mei S., Melchior M., Merlin E., Meylan G., Mora A.,
Moresco M., Moscardini L., Nakajima R., Neissner C., Nichol R.C.,
Niemi S.-M., Nightingale J.W., Padilla C., Paltani S., Pasian F.,
Pedersen K., Percival W.J., Pettorino V., Polenta G., Poncet M., Popa L.A.,
Raison F., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M.,
Saglia R., Sakr Z., Sapone D., Schirmer M., Schneider P., Schrabback T.,
Secroun A., Seidel G., Sihvola E., Simon P., Sirignano C., Sirri G.,
Stanco L., Tallada-Crespi P., Taylor A.N., Tereno I., Tessore N., Toft S.,
Toledo-Moreo R., Torradeflot F., Tutusaus I., Valenziano L., Valiviita J.,
Vassallo T., Verdoes Kleijn G., Veropalumbo A., Wang Y., Weller J.,
Zacchei A., Zamorani G., Zerbi F.M., Zucca E., Ballardini M., Bolzonella M.,
Burigana C., Cabanac R., Calabrese M., Cappi A., Castro T.,
Escartin Vigo J.A., Gabarra L., Hemmati S., Macias-Perez J., Maoli R.,
Martin-Fleitas J., Mauri N., Monaco P., Nucita A.A., Pezzotta A.,
Poentinen M., Risso I., Scottez V., Sereno M., Tenti M., Tucci M., Viel M.,
Wiesmann M., Akrami Y., Andika I.T., Angora G., Anselmi S., Archidiacono M.,
Atrio-Barandela F., Bazzanini L., Bergamini P., Bertacca D., Bethermin M.,
Beutler F., Blot L., Borgani S., Brown M.L., Bruton S., Calabro A.,
Camacho Quevedo B., Caro F., Carvalho C.S., Cogato F., Conseil S.,
Cucciati O., Davini S., Desprez G., Diaz-Sanchez A., Di Domizio S.,
Diego J.M., Duc P.-A., Duret V., Elkhashab M.Y., Enia A., Fang Y.,
Finoguenov A., Franco A., Ganga K., Gasparetto T., Gaztanaga E.,
Giacomini F., Gianotti F., Gozaliasl G., Guidi M., Gutierrez C.M., Hall A.,
Hernandez-Monteagudo C., Hildebrandt H., Hjorth J., Kajava J.J.E., Kang Y.,
Kansal V., Karagiannis D., Kiiveri K., Kim J., Kirkpatrick C.C., Lepori F.,
Leroy G., Lesci G.F., Lesgourgues J., Liaudat T.I., Liu S.J.,
Magliocchetti M., Magnier E.A., Mannucci F., Martins C.J.A.P., Maurin L.,
Miluzio M., Moretti C., Morgante G., Naidoo K., Navarro-Alsina A.,
Nesseris S., Paoletti D., Passalacqua F., Paterson K., Patrizii L.,
Pisani A., Potter D., Pratt G.W., Quai S., Radovich M., Rojas K., Roster W.,
Sacquegna S., Sahlen M., Sanders D.B., Sarpa E., Scarlata C., Schneider A.,
Schultheis M., Sciotti D., Sellentin E., Smith L.C., Tanidis K., Tao C.,
Tarsitano F., Testera G., Teyssier R., Tosi S., Troja A., Venhola A.,
Vergani D., Vernardos G., Verza G., Vinciguerra S., Walton N.A.,
Wright A.H., Yeung H.W.
<Astron. Astrophys. 712, A207 (2026)>
=2026A&A...712A.207E 2026A&A...712A.207E (SIMBAD/NED BibCode)
ADC_Keywords: Gravitational lensing ; Photometry ; Optical ; Infrared
Keywords: gravitational lensing: strong - methods: data analysis -
techniques: image processing - surveys
Abstract:
We present an end-to-end, iterative pipeline for efficient
identification of strong galaxy-galaxy lensing systems, applied to
the Euclid Q1 imaging data. Starting from VIS catalogues, we reject
point sources, apply a magnitude cut (IE≤24) on deflectors, and run a
pixel-level artefact/noise filter to build 96x96 pix cutouts;
VIS+NISP colour composites are constructed with a VIS-anchored
luminance scheme that preserves VIS morphology and NISP colour
contrast. A VIS-only seed classifier supplies clear positives and
typical impostors, from which we curate a morphology-balanced negative
set and augment scarce positives. Among the six compact CNNs studied
initially, a modified VGG16 (GlobalAveragePooling + 256/128 dense
layers with the last nine layers trainable) performs best; the
training set grows from 27 seed lenses (augmented 67x to 1809) plus
2000 negatives to a colour dataset of 30686 images. 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.
Description:
Catalog of classified strong gravitational lens candidates identified
in Euclid Q1 using the machine-learning framework described in the
paper.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
tablee1.dat 216 367 Catalog of classified strong gravitational lens
candidates identified in Euclid Q1
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See also:
II/388 : Euclid final merged catalog. Q1 data release (Euclid Coll., 2025)
Byte-by-byte Description of file: tablee1.dat
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Bytes Format Units Label Explanations
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1- 3 I3 --- Index Internal cutout identifier
5- 37 A33 --- cutoutName Alternative identifier composed of the
Euclid mosaic tile and cutout identifier
39- 63 A25 --- objName Euclid object identifierR
(EUCLJHHMMSS.ss+DDMMSS.ss)
65- 83 F19.15 deg RAdeg Right ascension (J2000)
85-103 F19.15 deg DEdeg Declination (J2000)
105-113 F9.6 mag VISmag Euclid VIS-band magnitude
derived from FLUXDETECTIONTOTAL
115-123 F9.6 mag Ymag Euclid Y-band magnitude
derived from FLUXY2FWHM_APER
125-133 F9.6 mag Hmag Euclid H-band magnitude
derived from FLUXH2FWHM_APER
135-143 F9.6 mag Jmag ? Euclid J-band magnitude
derived from FLUXJ2FWHM_APER
145-146 A2 --- Grade Processed human-expert classification grade
148 A1 --- New [Y/N] Indicates whether the lens candidate
is newly identified in this work (Y/N)
150-169 F20.16 --- RawScore Raw score produced by the
machine-learning model
171-216 A46 --- Notes References and additional remarks (1)
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Note (1): References :
E25 = Euclid Collaboration: Ecker et al. (2025, A&A, in prep.)
L25 = Euclid Collaboration: Lines et al. (2026A&A...711A..28E 2026A&A...711A..28E)
R25 = Euclid Collaboration: Rojas et al. (2026A&A...711A..27E 2026A&A...711A..27E)
W25 = Euclid Collaboration: Walmsley et al. (2026A&A...711A..26E 2026A&A...711A..26E)
Jacob+19 = Jocob et al., 2019MNRAS.484.5330J 2019MNRAS.484.5330J, Cat. J/MNRAS/484/5330
Storfer+22 = Storfer et al., 2024ApJS..274...16S 2024ApJS..274...16S
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Acknowledgements:
Xinmei Xu, xinmeix(at)uci.edu
License: CC-BY-4.0
(End) Patricia Vannier [CDS] 15-Jun-2026