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We present the results of our pipeline for discovering strong gravitational lenses in the ongoing Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We successfully train the deep residual neural network (ResNet) based on…

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered $\sim 3500$ new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a…

Ongoing and future imaging surveys represent significant improvements in depth, area and seeing compared to current data-sets. These improvements offer the opportunity to discover up to three orders of magnitude more galaxy-galaxy strong…

Cosmology and Nongalactic Astrophysics · Physics 2015-09-23 Thomas E Collett

The Dark Energy Survey (DES) will be unprecedented in its ability to probe exceptionally large cosmic volumes to relatively faint optical limits. Primarily designed for the study of comparatively low redshift (z<2) galaxies with the aim of…

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the…

We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different…

Instrumentation and Methods for Astrophysics · Physics 2021-04-06 H. G. Khachatryan

Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and…

Astrophysics of Galaxies · Physics 2023-08-17 Keerthi Vasan G. C. , Stephen Sheng , Tucker Jones , Chi Po Choi , James Sharpnack

Galaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating the properties of dark matter and dark energy, as well as providing the opportunity to study the distant universe at flux levels and…

Astrophysics of Galaxies · Physics 2026-01-19 Zhejian Zhang , Nan Li , Shude Mao , Hu Zou , Zizhao He , Mingxiang Fu , Shenzhe Cui

Accurate redshift determinations of both lenses and sources are critical for confirming strong-lens systems and fully realizing their scientific value. However, the thousands of strong-lens candidates now routinely discovered in wide-field…

Astrophysics of Galaxies · Physics 2025-09-10 Yiping Shu , Shen Li

Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…

Cosmology and Nongalactic Astrophysics · Physics 2023-08-09 G. Angora , P. Rosati , M. Meneghetti , M. Brescia , A. Mercurio , C. Grillo , P. Bergamini , A. Acebron , G. Caminha , M. Nonino , L. Tortorelli , L. Bazzanini , E. Vanzella

We present results from a systematic search for strong gravitational lenses in the GOODS ACS data. The search technique involves creating a sample of likely lensing galaxies, which we define as massive early-type galaxies in a redshift…

Astrophysics · Physics 2009-11-10 C. D. Fassnacht , L. A. Moustakas , S. Casertano , H. C. Ferguson , R. A. Lucas , Y. Park

More than one hundred galaxy-scale strong gravitational lens systems have been found by searching for the emission lines coming from galaxies with redshifts higher than the lens galaxies. Based on this spectroscopic-selection method, we…

Astrophysics of Galaxies · Physics 2018-10-12 Rui Li , Yiping Shu , Jianlin Su , Haicheng Feng , Guobao Zhang , Jiancheng Wang , Hongtao Liu

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong…

Instrumentation and Methods for Astrophysics · Physics 2022-06-23 George Stein , Jacqueline Blaum , Peter Harrington , Tomislav Medan , Zarija Lukic

We apply a novel model based on convolutional neural networks (CNNs) to identify gravitationally-lensed galaxies in multi-band imaging of the Hyper Suprime Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is applied to a…

Future large-scale surveys with high resolution imaging will provide us with a few $10^5$ new strong galaxy-scale lenses. These strong lensing systems however will be contained in large data amounts which are beyond the capacity of human…

Instrumentation and Methods for Astrophysics · Physics 2018-03-14 C. Schaefer , M. Geiger , T. Kuntzer , J-P. Kneib

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential…

Astrophysics of Galaxies · Physics 2024-01-29 Euclid Collaboration , L. Leuzzi , M. Meneghetti , G. Angora , R. B. Metcalf , L. Moscardini , P. Rosati , P. Bergamini , F. Calura , B. Clément , R. Gavazzi , F. Gentile , M. Lochner , C. Grillo , G. Vernardos , N. Aghanim , A. Amara , L. Amendola , S. Andreon , N. Auricchio , S. Bardelli , C. Bodendorf , D. Bonino , E. Branchini , M. Brescia , J. Brinchmann , S. Camera , V. Capobianco , C. Carbone , J. Carretero , S. Casas , M. Castellano , S. Cavuoti , A. Cimatti , R. Cledassou , G. Congedo , C. J. Conselice , L. Conversi , Y. Copin , L. Corcione , F. Courbin , H. M. Courtois , M. Cropper , A. Da Silva , H. Degaudenzi , J. Dinis , F. Dubath , X. Dupac , S. Dusini , M. Farina , S. Farrens , S. Ferriol , M. Frailis , E. Franceschi , M. Fumana , S. Galeotta , B. Gillis , C. Giocoli , A. Grazian , F. Grupp , L. Guzzo , S. V. H. Haugan , W. Holmes , I. Hook , F. Hormuth , A. Hornstrup , P. Hudelot , K. Jahnke , B. Joachimi , M. Kümmel , E. Keihänen , S. Kermiche , A. Kiessling , T. Kitching , M. Kunz , H. Kurki-Suonio , P. B. Lilje , V. Lindholm , I. Lloro , D. Maino , E. Maiorano , O. Mansutti , O. Marggraf , K. Markovic , N. Martinet , F. Marulli , R. Massey , E. Medinaceli , S. Mei , M. Melchior , Y. Mellier , E. Merlin , G. Meylan , M. Moresco , E. Munari , S. -M. Niemi , J. W. Nightingale , T. Nutma , C. Padilla , S. Paltani , F. Pasian , K. Pedersen , V. Pettorino , S. Pires , G. Polenta , M. Poncet , F. Raison , A. Renzi , J. Rhodes , G. Riccio , E. Romelli , M. Roncarelli , E. Rossetti , R. Saglia , D. Sapone , B. Sartoris , M. Schirmer , P. Schneider , A. Secroun , G. Seidel , S. Serrano , C. Sirignano , G. Sirri , L. Stanco , P. Tallada-Crespí , A. N. Taylor , I. Tereno , R. Toledo-Moreo , F. Torradeflot , I. Tutusaus , L. Valenziano , T. Vassallo , A. Veropalumbo , Y. Wang , J. Weller , G. Zamorani , J. Zoubian , E. Zucca , A. Boucaud , E. Bozzo , C. Colodro-Conde , D. Di Ferdinando , R. Farinelli , J. Graciá-Carpio , N. Mauri , C. Neissner , V. Scottez , M. Tenti , A. Tramacere , Y. Akrami , V. Allevato , C. Baccigalupi , M. Ballardini , F. Bernardeau , A. Biviano , S. Borgani , A. S. Borlaff , H. Bretonnière , C. Burigana , R. Cabanac , A. Cappi , C. S. Carvalho , G. Castignani , T. Castro , K. C. Chambers , A. R. Cooray , J. Coupon , S. Davini , S. de la Torre , G. De Lucia , G. Desprez , S. Di Domizio , H. Dole , J. A. Escartin Vigo , S. Escoffier , I. Ferrero , L. Gabarra , K. Ganga , J. Garcia-Bellido , E. Gaztanaga , K. George , G. Gozaliasl , H. Hildebrandt , M. Huertas-Company , J. J. E. Kajava , V. Kansal , C. C. Kirkpatrick , L. Legrand , A. Loureiro , M. Magliocchetti , G. Mainetti , R. Maoli , M. Martinelli , C. J. A. P. Martins , S. Matthew , L. Maurin , P. Monaco , G. Morgante , S. Nadathur , A. A. Nucita , M. Pöntinen , L. Patrizii , V. Popa , C. Porciani , D. Potter , P. Reimberg , A. G. Sánchez , Z. Sakr , A. Schneider , M. Sereno , P. Simon , A. Spurio Mancini , J. Stadel , J. Steinwagner , R. Teyssier , J. Valiviita , M. Viel , I. A. Zinchenko , H. Domínguez Sánchez