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We report the discovery of seven new, very bright gravitational lens systems from our ongoing gravitational lens search, the Sloan Bright Arcs Survey (SBAS). Two of the systems are confirmed to have high source redshifts z=2.19 and z=2.94.…

We present RingFinder, a tool for finding galaxy-scale strong gravitational lenses in multiband imaging data. By construction, the method is sensitive to configurations involving a massive foreground early-type galaxy and a faint,…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-19 Raphael Gavazzi , Philip J. Marshall , Tommaso Treu , Alessandro Sonnenfeld

Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with…

A total of 10 good candidates for gravitational lensing have been discovered in the WFPC2 images from the HST Medium Deep Survey (MDS) and archival primary observations. These candidate lenses are unique HST discoveries, i.e. they are faint…

Astrophysics · Physics 2009-10-31 K. U. Ratnatunga , R. E. Griffiths , E. J. Ostrander

Spitzer IRAC selection is a powerful tool for identifying luminous AGN. For deep IRAC data, however, the AGN selection wedges currently in use are heavily contaminated by star-forming galaxies, especially at high redshift. Using the large…

In this work, six convolutional neural networks (CNNs) have been trained based on %different feature images and arrays from the database including 15,638 superflare candidates on solar-type stars, which are collected from the three-years…

Solar and Stellar Astrophysics · Physics 2022-09-19 Zuo-Lin Tu , Qin Wu , Wenbo Wang , G. Q. Zhang , Zi-Ke Liu , F. Y. Wang

We present the first catalogue of strong lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering $63.1\,\mathrm{deg^2}$). This catalogue is the result of the visual inspection of 1260 cluster fields. Each…

Cosmology and Nongalactic Astrophysics · Physics 2025-03-20 Euclid Collaboration , P. Bergamini , M. Meneghetti , A. Acebron , B. Clément , M. Bolzonella , C. Grillo , P. Rosati , D. Abriola , J. A. Acevedo Barroso , G. Angora , L. Bazzanini , R. Cabanac , B. C. Nagam , A. R. Cooray , G. Despali , G. Di Rosa , J. M. Diego , M. Fogliardi , A. Galan , R. Gavazzi , G. Granata , N. B. Hogg , K. Jahnke , L. Leuzzi , T. Li , M. Lombardi , G. Mahler , A. Manjón-García , R. B. Metcalf , M. Oguri , C. Olave , J. M. Palencia , J. Richard , K. Rojas , L. R. Ecker , C. Scarlata , M. Schirmer , S. Schuldt , D. Sluse , G. P. Smith , C. Tortora , G. Vernardos , G. L. Walth , J. Wilde , Y. Xie , M. Zumalacarregui , N. Aghanim , B. Altieri , A. Amara , L. Amendola , S. Andreon , N. Auricchio , H. Aussel , C. Baccigalupi , M. Baldi , A. Balestra , S. Bardelli , A. Basset , P. Battaglia , R. Bender , A. Biviano , A. Bonchi , D. Bonino , E. Branchini , M. Brescia , J. Brinchmann , A. Caillat , S. Camera , G. Cañas-Herrera , V. Capobianco , C. Carbone , J. Carretero , S. Casas , F. J. Castander , M. Castellano , G. Castignani , S. Cavuoti , K. C. Chambers , A. Cimatti , C. Colodro-Conde , G. Congedo , C. J. Conselice , L. Conversi , Y. Copin , F. Courbin , H. M. Courtois , M. Cropper , A. Da Silva , H. Degaudenzi , G. De Lucia , A. M. Di Giorgio , C. Dolding , H. Dole , F. Dubath , X. Dupac , S. Dusini , A. Ealet , S. Escoffier , M. Fabricius , M. Farina , R. Farinelli , F. Faustini , S. Ferriol , F. Finelli , P. Fosalba , S. Fotopoulou , M. Frailis , E. Franceschi , M. Fumana , S. Galeotta , K. George , B. Gillis , C. Giocoli , P. Gómez-Alvarez , J. Gracia-Carpio , B. R. Granett , A. Grazian , F. Grupp , L. Guzzo , S. V. H. Haugan , H. Hoekstra , W. Holmes , F. Hormuth , A. Hornstrup , P. Hudelot , M. Jhabvala , B. Joachimi , E. Keihänen , S. Kermiche , A. Kiessling , M. Kilbinger , R. Kohley , B. Kubik , K. Kuijken , M. Kümmel , M. Kunz , H. Kurki-Suonio , O. Lahav , R. Laureijs , Q. Le Boulc'h , A. M. C. Le Brun , D. Le Mignant , P. Liebing , S. Ligori , P. B. Lilje , V. Lindholm , I. Lloro , G. Mainetti , D. Maino , E. Maiorano , O. Mansutti , S. Marcin , O. Marggraf , M. Martinelli , N. Martinet , F. Marulli , R. Massey , S. Maurogordato , E. Medinaceli , S. Mei , M. Melchior , Y. Mellier , E. Merlin , G. Meylan , A. Mora , M. Moresco , L. Moscardini , S. Mourre , 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 , R. Rebolo , A. Renzi , J. Rhodes , G. Riccio , E. Romelli , M. Roncarelli , B. Rusholme , R. Saglia , Z. Sakr , D. Sapone , B. Sartoris , J. A. Schewtschenko , P. Schneider , A. Secroun , G. Seidel , M. Seiffert , S. Serrano , P. Simon , C. Sirignano , G. Sirri , A. Spurio Mancini , L. Stanco , J. Steinwagner , P. Tallada-Crespí , A. N. Taylor , H. I. Teplitz , I. Tereno , N. Tessore , S. Toft , R. Toledo-Moreo , F. Torradeflot , A. Tsyganov , I. Tutusaus , E. A. Valentijn , L. Valenziano , J. Valiviita , T. Vassallo , G. Verdoes Kleijn , A. Veropalumbo , Y. Wang , J. Weller , A. Zacchei , G. Zamorani , F. M. Zerbi , E. Zucca , V. Allevato , M. Ballardini , E. Bozzo , C. Burigana , A. Cappi , P. Casenove , D. Di Ferdinando , J. A. Escartin Vigo , L. Gabarra , J. Martín-Fleitas , S. Matthew , M. Maturi , N. Mauri , A. A. Nucita , A. Pezzotta , M. Pöntinen , C. Porciani , I. Risso , V. Scottez , M. Sereno , M. Tenti , M. Viel , M. Wiesmann , Y. Akrami , I. T. Andika , S. Anselmi , M. Archidiacono , F. Atrio-Barandela , C. Benoist , K. Benson , D. Bertacca , M. Bethermin , A. Blanchard , L. Blot , H. Böhringer , S. Borgani , M. L. Brown , S. Bruton , A. Calabro , B. Camacho Quevedo , F. Caro , C. S. Carvalho , T. Castro , F. Cogato , O. Cucciati , S. Davini , F. De Paolis , G. Desprez , A. Díaz-Sánchez , J. J. Diaz , S. Di Domizio , P. -A. Duc , A. Enia , Y. Fang , A. G. Ferrari , P. G. Ferreira , A. Finoguenov , A. Fontana , A. Franco , K. Ganga , J. García-Bellido , T. Gasparetto , V. Gautard , E. Gaztanaga , F. Giacomini , F. Gianotti , A. H. Gonzalez , G. Gozaliasl , M. Guidi , C. M. Gutierrez , A. Hall , W. G. Hartley , C. Hernández-Monteagudo , H. Hildebrandt , J. Hjorth , O. Ilbert , M. Jauzac , J. J. E. Kajava , Y. Kang , V. Kansal , D. Karagiannis , K. Kiiveri , C. C. Kirkpatrick , S. Kruk , J. Le Graet , L. Legrand , M. Lembo , F. Lepori , G. Leroy , G. F. Lesci , J. Lesgourgues , T. I. Liaudat , S. J. Liu , A. Loureiro , J. Macias-Perez , G. Maggio , M. Magliocchetti , F. Mannucci , R. Maoli , C. J. A. P. Martins , L. Maurin , M. Migliaccio , M. Miluzio , P. Monaco , C. Moretti , G. Morgante , C. Murray , S. Nadathur , K. Naidoo , A. Navarro-Alsina , S. Nesseris , F. Passalacqua , K. Paterson , L. Patrizii , A. Pisani , D. Potter , S. Quai , M. Radovich , P. Reimberg , P. -F. Rocci , G. Rodighiero , S. Sacquegna , M. Sahlén , D. B. Sanders , E. Sarpa , A. Schneider , M. Schultheis , D. Sciotti , E. Sellentin , F. Shankar , L. C. Smith , S. A. Stanford , K. Tanidis , C. Tao , G. Testera , R. Teyssier , S. Tosi , A. Troja , M. Tucci , C. Valieri , A. Venhola , D. Vergani , G. Verza , P. Vielzeuf , N. A. Walton , E. Soubrie , D. Scott

Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…

Cosmology and Nongalactic Astrophysics · Physics 2026-05-06 M. Fogliardi , M. Meneghetti , C. Giocoli , L. Moscardini , P. Rosati , L. Leuzzi , G. Angora , L. Bazzanini , C. Spinelli

This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with absolute…

Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion.…

Computer Vision and Pattern Recognition · Computer Science 2015-03-24 Nikolaos Karianakis , Thomas J. Fuchs , Stefano Soatto

Gravitational lens systems containing lensed quasars are important as cosmological probes, as diagnostics of structural properties of the lensing galaxies and as tools to study the quasars themselves. The largest lensed quasar sample is the…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-30 Neal Jackson , Hayden Rampadarath , Eran O. Ofek , Masamune Oguri , Min-Su Shin

The Convolutional Neural Network (CNN) has shown impressive performance in image classification because of its strong learning capabilities. However, it demands a substantial and balanced dataset for effective training. Otherwise, networks…

Computer Vision and Pattern Recognition · Computer Science 2025-02-17 Arun Kunwar , Dibakar Raj Pant , Jukka Heikkonen , Rajeev Kanth

We present the full sample of 118 galaxy-scale strong-lens candidates in the Sloan Lens ACS (SLACS) Survey for the Masses (S4TM) Survey, which are spectroscopically selected from the final data release of the Sloan Digital Sky Survey.…

Next generation large sky surveys will observe up to billions of galaxies for which basic structural parameters are needed to study their evolution. This is a challenging task that, for ground-based observations, is complicated by seeing…

Astrophysics of Galaxies · Physics 2022-05-04 R. Li , N. R. Napolitano , N. Roy , C. Tortora , F. La Barbera , A. Sonnenfeld , C. Qiu , S. Liu

Many strong gravitational lenses lie in complex environments, such as poor groups of galaxies, that significantly bias conclusions from lens analyses. We are undertaking a photometric survey of all known galaxy-mass strong lenses to…

Astrophysics · Physics 2010-11-11 K. A. Williams , I. Momcheva , C. R. Keeton , A. I. Zabludoff , J. Lehar

Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…

We apply a state-of-the-art transformer algorithm to 221 deg$^2$ of the Kilo Degree Survey (KiDS) to search for new strong gravitational lenses (SGL). We test four transformer encoders trained on simulated data from the Strong Lens Finding…

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in…

Astrophysics of Galaxies · Physics 2020-11-26 Dimitrios Tanoglidis , Aleksandra Ćiprijanović , Alex Drlica-Wagner