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Related papers: Strong lens systems search in the Dark Energy Surv…

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We present a systematic search for wide-separation (Einstein radius >1.5"), galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey on the Northern sky. With long time delays of a few days to weeks, such systems are…

Astrophysics of Galaxies · Physics 2021-04-08 R. Canameras , S. Schuldt , S. H. Suyu , S. Taubenberger , T. Meinhardt , L. Leal-Taixe , C. Lemon , K. Rojas , E. Savary

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…

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

We present spectroscopic confirmation of candidate strong gravitational lenses using the Keck Observatory and Very Large Telescope as part of our ASTRO 3D Galaxy Evolution with Lenses (AGEL) survey. We confirm that 1) search methods using…

We performed a systematic search for strong gravitational lenses using Hyper Suprime-Cam (HSC) data, focusing on galaxy-scale lenses combined with an environment analysis resulting in the identification of lensing clusters. To identify…

We have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic strong-lens simulations, deep neural network classification, and…

We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 8. We use deep residual neural networks, building on previous work presented in Huang…

Instrumentation and Methods for Astrophysics · Physics 2021-03-10 X. Huang , C. Storfer , A. Gu , V. Ravi , A. Pilon , W. Sheu , R. Venguswamy , S. Banka , A. Dey , M. Landriau , D. Lang , A. Meisner , J. Moustakas , A. D. Myers , R. Sajith , E. F. Schlafly , D. J. Schlegel

We report the observation and confirmation of the first group- and cluster-scale strong gravitational lensing systems found in Dark Energy Survey (DES) data. Through visual inspection of data from the Science Verification (SV) season, we…

Recent wide-field galaxy surveys have led to an explosion in the number of galaxy-scale strong gravitational lens candidates. However, the vast majority of them feature massive luminous red galaxies as the main deflectors, with late-type…

Astrophysics of Galaxies · Physics 2026-02-18 J. A. Acevedo Barroso , B. Clément , F. Courbin , R. Gavazzi , C. Lemon , K. Rojas , D. Scott , S. Gwyn , F. Hammer , M. J. Hudson , E. A. Magnier

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

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

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

We carry out a search for strong-lens systems containing high-redshift lens galaxies with the goal of extending strong-lensing-assisted galaxy evolutionary studies to earlier cosmic time. Two strong-lens classifiers are constructed from a…

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

Bright galaxy-galaxy strong lenses are much more powerful than lensed quasars for measuring the mass profiles of galaxies, but until this year only a handful have been known. Here we present five new examples, identified via the optimal…

Astrophysics · Physics 2009-11-11 J. P. Willis , P. C. Hewett , S. J. Warren , S. Dye , N. Maddox

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…