<i>Euclid</i> : Finding strong gravitational lenses in the early release observations using convolutional neural networks

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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Journal
Astronomy and Astrophysics
Published
2025-09-03
DOI
https://doi.org/10.1051/0004-6361/202554132
Citations
3
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
3.44

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article

Euclid : Finding strong gravitational lenses in the early release observations using convolutional neural networks

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3 citations
Astronomy and Astrophysics
Reservoir Engineering and Simulation Methods
3.44
article

Euclid : Finding strong gravitational lenses in the early release observations using convolutional neural networks

D Sapone, R. Laureijs, C. Baccigalupi, G. Seidel, Will J. Percival, P. Tallada-Crespí, G. Riccio, Y. Mellier, N. Auricchio, J.-L. Starck, D. Le Mignant, C. Sirignano, A. Secroun, F. Marulli, I. Hook, Pablo Corcho-Caballero, G. Sirri, Leonidas A. Moustakas, K George, F. Courbin, V. Scottez, Y. Copin, A. Cimatti, M. Jhabvala, A. Díaz‐Sánchez, Y. Copin, L. Guzzo, T.T. Thai, A.R. Cooray, V. Capobianco, T. Schrabback, L. Delchambre, M. Schirmer, S. V. H. Haugan, J. Skottfelt, R. Massey, S. Paltani, A. Tsyganov, T. Vassallo, Daniel Stern, L. Valenziano, C. Neissner, I. Tutusaus, M. Cropper, R. Toledo-Moreo, M. Moresco, C. Giocoli, G Congedo, Predrag Matavulj, A. Da Silva, M. Meneghetti, G. De Lucia, J.W Nightingale, A. Veropalumbo, A. Caillat, C Colodro-Conde, S. Ferriol, C. Burigana, A. M. Di Giorgio, Christopher J. Conselice, B. Joachimi, F. Torradeflot, J. Carretero, E. Zucca, J.A. Acevedo Barroso, A Manjón-García, B. Clément, E. Maiorano, R. B. Metcalf, A. Hornstrup, F Pasian, O. Marggraf, P. Schneider, M. Pöntinen, L. Stanco, E. Medinaceli, N. Martinet, E. Branchini, A. Balestra, K. Jahnkę, V. Pettorino, M. Farina, S. Camera, R. Saglia, G. Vernardos, Stephen Serjeant, J. Weller, M. Castellano, J. Brinchmann, Emily Moravec, R. B. Metcalf, E. Franceschi, E. A. Valentijn, Y Wang, B. Gillis, P. Gómez-Álvarez, W. Gillard, R. Farinelli, S. Farrens, J. Wilde, Thomas E. Collett, Antonio M. Mora, K. Markovič, I.T Andika, K. Pedersen, N. Martinet, Anely Maciel de Melo, Mike Walmsley, F. Hormuth, S Casas, F. Dubath, L.V.E. Koopmans, A. Grazian, D. Bonino, E. Merlin, H. Degaudenzi, V. Lindholm, M. Fumana, F. Raison, R. Rébolo, G. Verdoes Kleijn, S. Pires, E. Keihänen, G. Castignani, M. Melchior, M. Frailis, Conor M. O’Riordan, A. Renzi, P. Hudelot, S. Andreon, R. Gavazzi, C. Carbone, G. Despali, B. Altieri, F. Grupp, S. Kermiche, H.I Teplitz, J. Steinwagner, J. Rhodes, S Dusini, M. Roncarelli, J. M. Diego, O. Mansutti, A.G. Sánchez, D. Sluse, P. B. Lilje, M. Fabricius, M. Poncet, A.N. Taylor, S. Schuldt, L.A. Popa, Kathryn McCarthy, M. Kunz, S. Ligori, M Brescia, G. Mainetti, J. Dinis, M. Kümmel, S Cavuoti, L. Pozzetti, C. Padilla, H. M. Courtois, R. C. Nichol, B.C Nagam, Valerio Busillo, R Pearce-Casey, L. Ulivi, K. Rojas, Ryota Nakajima, M. Baldi, B. Sartoris, E. Rossetti, A Amara, S Serrano, E. Romelli, I. Tereno, L.R Ecker, P Battaglia, G. Meylan, D. Tavagnacco, S Bardelli, G Polenta, H. Kurki‐Suonio, Z. Sakr, W. A. Holmes, B. Kubik, I. Lloro, K Kuijken, S.-M. Niemi, L. Moscardini, C. A. J. Duncan, X. Dupac, L. Leuzzi
article en
3 citations

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.

Astronomy and AstrophysicsVol. 702
Université Claude Bernard Lyon 1 (FR), University of Geneva (CH), Institució Catalana de Recerca i Estudis Avançats (ES), Leiden University (NL), California Institute of Technology (US), University of Copenhagen (DK), Centre National de la Recherche Scientifique (FR), University of Minnesota (US), Goddard Space Flight Center (US), Jet Propulsion Laboratory (US), American Museum of Natural History (US), Universidade Federal de Ciências da Saúde de Porto Alegre (BR), Université Toulouse III - Paul Sabatier (FR), Consejo Superior de Investigaciones Científicas (ES), University of Bonn (DE), Scuola Internazionale Superiore di Studi Avanzati (IT), University of Padua (IT), University of Lisbon (PT), University of Waterloo (CA), Universidad de La Laguna (ES), Lehman College (US), University of Groningen (NL), Université Fédérale de Toulouse Midi-Pyrénées (FR), Lyon College (US), Université Catholique de Lille (FR), Perimeter Institute (CA), University of Oslo (NO), University of Toronto (CA), Institute for High Energy Physics (ES), University of Milan (IT), Universität Innsbruck (AT), University of Saint Joseph (US), University of Trento (IT), The Open University (GB), University of California, Irvine (US), Université Paris Cité (FR), Aix-Marseille Université (FR), Heidelberg University (DE), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Centre National d'Études Spatiales (FR), Arcetri Astrophysical Observatory (IT), Helsinki Institute of Physics (FI), Astronomical Observatory of Rome (IT), Trieste Astronomical Observatory (IT), Institut d'Astrophysique de Paris (FR), Astronomical Observatory of Capodimonte (IT), European Space Agency (FR), Brera Astronomical Observatory (IT), University of Surrey (GB), FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH), United Kingdom Space Agency (GB), Universidad Politécnica de Cartagena (ES), Department of Space (IN), Sorbonne Université (FR), Park University (US), Osservatorio astronomico di Bologna (IT), Astrophysique, Instrumentation et Modélisation (FR), Institute of Astrophysics and Space Sciences (PT), Max Planck Institute for Extraterrestrial Physics (DE), Institut d'Estudis Espacials de Catalunya (ES), European Space Astronomy Centre (ES), Istituto Nazionale di Fisica Nucleare, Sezione di Torino (IT), Institute for Space Astrophysics and Planetology (IT), Max Planck Institute for Astrophysics (DE), Institut Català de Ciències del Clima (ES), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT), Osservatorio Astrofisico di Torino (IT), SKA Observatory (GB), Istituto Nazionale di Fisica Nucleare, Sezione di Napoli (IT), Max Planck Institute for Astronomy (DE), Institute of Space Sciences (ES), Laboratoire de Physique Théorique (FR), DAWN Center for Independent Living (US), Istituto di Radioastronomia di Bologna (IT), Federico II University Hospital (IT), Unidades Centrales Científico-Técnicas (ES), Institute of Theoretical Physics (CN), Istituto Nazionale di Fisica Nucleare, Sezione di Genova (IT), CEA Paris-Saclay (FR), Naples Anesthesia & Physician Associates (US), Instituto de Astrofísica de Canarias (ES), Felix Scholarship (GB), Astronomical Institute of the Slovak Academy of Sciences (SK), Allen Institute (US), Institute of Space Science - INFLPR Subsidiary (RO), Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (FR), Fractal Systems (Canada) (CA), Istituto Nazionale di Fisica Nucleare, Sezione di Bologna (IT), Port d'Informació Científica (ES), Istituto Nazionale di Fisica Nucleare, Sezione di Trieste (IT), Institute for Fundamental Physics of the Universe (IT), Instituto de Física de Cantabria (ES), St. Joseph Medical Center (US), Istituto Nazionale di Fisica Nucleare, Sezione di Roma I (IT), Osservatorio Astronomico di Padova (IT), National Astronomical Observatory of Japan (JP), European Space Research Institute (IT), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), Institut de Recherche en Astrophysique et Planétologie (FR), Infrared Processing and Analysis Center (US), Osservatorio di Astrofisica e Scienza dello Spazio, European Space Research and Technology Centre (NL), University of Florence (IT), University College London (GB), École Polytechnique Fédérale de Lausanne (CH), University of Turin (IT), Technical University of Munich (DE), University of Portsmouth (GB), Lancaster University (GB), University of Chile (CL), Heidelberg Institute for Theoretical Studies (DE), Open University Malaysia (MY), Ludwig-Maximilians-Universität München (DE), University of Genoa (IT), Newcastle University (GB), National Institute for Astrophysics (IT), RWTH Aachen University (DE), Netherlands Institute for Radio Astronomy (NL), University of Bologna (IT), Technical University of Denmark (DK), University of Edinburgh (GB)
National Science Foundation, National Aeronautics and Space Administration, California Institute of Technology, Ministerio de Ciencia, Innovación y Universidades, European Space Agency, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Magyar Tudományos Akadémia, Agenzia Spaziale Italiana, National Astronomical Observatory of Japan, Agenția Spațială Română, Nederlandse Onderzoekschool Voor Astronomie, Norsk Romsenter, Jet Propulsion Laboratory, Fundação para a Ciência e a Tecnologia, Agencia Estatal de Investigación
Openalex Percentile: Top 7%
Reservoir Engineering and Simulation Methods
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