: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field
Year: 2025
Type: article
Abstract: The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. As a result, machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates, such that the usage of CNNs in lens identification has increased. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate, thus producing a pure and complete sample of strong lens candidates from Euclid with a limited need for visual inspection. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. This work is vital in preparing our CNN-based detection pipelines to be able to produce a pure sample of the >100 000 strong gravitational lensing systems widely predicted for Euclid. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just ∼11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artifacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected ∼105 lensing systems in Euclid, this implies 106 objects for human classification, which while very large is not in principle intractable and not without precedent.
Authors: R. Pearce-Casey, B. C. Nagam, J. Wilde, V. Busillo, L. Ulivi, I. T. Andika, A. Manjón-García, L. Leuzzi, P. Matavulj, S. Serjeant, M. Walmsley, J. A. Acevedo Barroso, C. M. O’Riordan, B. Clément, C. Tortora, T. E. Collett, F. Courbin, R. Gavazzi, R. B. Metcalf, R. Cabanac, H. M. Courtois, J. Crook-Mansour, L. Delchambre, G. Despali, L. R. Ecker, A. Franco, P. Holloway, K. Jahnke, G. Mahler, L. Marchetti, A. Melo, M. Meneghetti, O. Müller, A. A. Nucita, J. Pearson, K. Rojas, C. Scarlata, S. Schuldt, D. Sluse, S. H. Suyu, M. Vaccari, S. Vegetti, A. Verma, G. Vernardos, M. Bolzonella, M. Kluge, T. Saifollahi, M. Schirmer, C. Stone, A. Paulino-Afonso, L. Bazzanini, N. B. Hogg, L. V. E. Koopmans, S. Kruk, F. Mannucci, J. M. Bromley, A. Díaz-Sánchez, H. J. Dickinson, D. M. Powell, H. Bouy, R. Laureijs, B. Altieri, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S.-M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, C. Burigana, M. Calabrese, A. Mora, M. Pöntinen, V. Scottez, M. Viel, B. Margalef-Bentabol
Institutions: The Open University, Max Planck Institute for Astrophysics, Technical University of Munich, Universidad Politécnica de Cartagena, Osservatorio di Astrofisica e Scienza dello Spazio, National Institute for Astrophysics, University of Bologna, University of Toronto, University of Manchester, École Polytechnique Fédérale de Lausanne, Astronomical Observatory of Capodimonte, University of Portsmouth, Institució Catalana de Recerca i Estudis Avançats, Institut de Ciències del Cosmos, Universitat de Barcelona, Centre National de la Recherche Scientifique, Aix-Marseille Université, Institut d'Astrophysique de Paris, Sorbonne Université, Université Toulouse III - Paul Sabatier, Institut de Recherche en Astrophysique et Planétologie, Lyon 1 Université, Institut National de Physique Nucléaire et de Physique des Particules, Institut de Physique des 2 Infinis de Lyon, University of Cape Town, University of Liège, Max Planck Institute for Extraterrestrial Physics, Ludwig-Maximilians-Universität München, University of Oxford, Max Planck Institute for Astronomy, University of Minnesota, University of Milan, Istituto di Radioastronomia di Bologna, Inter-university Institute for Data Intensive Astronomy, Université de Strasbourg, Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, FHNW University of Applied Sciences and Arts Northwestern Switzerland, University of Groningen, Arcetri Astrophysical Observatory, Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, University of Salento, Durham University
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Aeronautics and Space Administration, Ministerio de Ciencia, Innovación y Universidades, Department of Science and Innovation, South Africa, European Space Agency, European Commission, National Research Foundation, Deutsche Forschungsgemeinschaft, University of Toronto, Magyar Tudományos Akadémia, Agenzia Spaziale Italiana, Max-Planck-Gesellschaft, National Astronomical Observatory of Japan, Agenția Spațială Română, Norsk Romsenter, Science and Technology Facilities Council, Fundação para a Ciência e a Tecnologia, Agencia Estatal de Investigación
OpenAlex ID: W6959563358
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