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相关论文: Strong lens systems search in the Dark Energy Surv…

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We present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We describe our object selection,…

From the nature of dark matter to the rate of expansion of our Universe, observations of distant galaxies distorted through strong gravitational lensing have the potential to answer some of the major open questions in astrophysics. Modeling…

宇宙学与河外天体物理 · 物理学 2022-10-18 Siddharth Mishra-Sharma , Ge Yang

The Dark Energy Spectroscopic Instrument (DESI) is carrying out a 5-year survey that aims to measure the redshifts of tens of millions of galaxies and quasars, including 8 million luminous red galaxies (LRGs) in the redshift range of…

The environments of deflectors in strong lensing systems affect our ability to test cosmological models and constrain evolutionary properties of galaxies. Here we measure the deflector scale (Einstein mass) and deflector environment (halo…

The study of strong-lensing systems conventionally involves constructing a mass distribution that can reproduce the observed multiply-imaging properties. Such mass reconstructions are generically non-unique. Here, we present an alternative…

星系天体物理 · 物理学 2021-06-30 Philipp Denzel , Sampath Mukherjee , Prasenjit Saha

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…

The identification of strong gravitational lenses in large surveys has historically been a rather time consuming exercise. Early data from the Herschel Astrophysical Terahertz Large Area Survey (Herschel-ATLAS) demonstrate that lenses can…

宇宙学与河外天体物理 · 物理学 2012-06-22 Jo Short , Elizabeth Pearson , Peter Coles , Steve Eales

We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities faced when modelling real image data, such as…

天体物理仪器与方法 · 物理学 2019-07-24 James Pearson , Nan Li , Simon Dye

We present strong gravitational lensing models for 37 galaxy clusters from the SDSS Giant Arcs Survey. We combine data from multi-band Hubble Space Telescope WFC3imaging, with ground-based imaging and spectroscopy from Magellan, Gemini,…

We describe a new method of combining optical and infrared photometry to select Luminous Red Galaxies (LRGs) at redshifts $z > 0.6$. We explore this technique using a combination of optical photometry from CFHTLS and HST, infrared…

星系天体物理 · 物理学 2015-04-27 Abhishek Prakash , Timothy C. Licquia , Jeffrey A. Newman , Sandhya M. Rao

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller…

宇宙学与河外天体物理 · 物理学 2020-01-29 Ana Diaz Rivero , Cora Dvorkin

We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short).…

Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Einstein radii is related to the dark energy equation of state…

星系天体物理 · 物理学 2025-03-20 Euclid Collaboration , T. Li , T. E. Collett , M. Walmsley , N. E. P. Lines , K. Rojas , J. W. Nightingale , W. J. R. Enzi , L. A. Moustakas , C. Krawczyk , R. Gavazzi , G. Despali , P. Holloway , S. Schuldt , F. Courbin , R. B. Metcalf , D. J. Ballard , A. Verma , B. Clément , H. Degaudenzi , A. Melo , J. A. Acevedo Barroso , L. Leuzzi , A. Manjón-García , R. Pearce-Casey , D. Sluse , C. Tortora , R. Massey , G. Mahler , A. More , N. Aghanim , B. Altieri , A. Amara , S. Andreon , N. Auricchio , H. Aussel , C. Baccigalupi , M. Baldi , A. Balestra , S. Bardelli , P. Battaglia , R. Bender , F. Bernardeau , A. Biviano , A. Bonchi , E. Branchini , M. Brescia , J. Brinchmann , S. Camera , G. Cañas-Herrera , V. Capobianco , C. Carbone , V. F. Cardone , J. Carretero , S. Casas , M. Castellano , G. Castignani , S. Cavuoti , K. C. Chambers , A. Cimatti , C. Colodro-Conde , G. Congedo , C. J. Conselice , L. Conversi , Y. Copin , H. M. Courtois , M. Cropper , A. Da Silva , G. De Lucia , A. M. Di Giorgio , C. Dolding , H. Dole , F. Dubath , C. A. J. Duncan , X. Dupac , S. Escoffier , M. Fabricius , M. Farina , R. Farinelli , F. Faustini , S. Ferriol , F. Finelli , S. Fotopoulou , M. Frailis , E. Franceschi , S. Galeotta , K. George , W. Gillard , B. Gillis , C. Giocoli , P. Gómez-Alvarez , J. Gracia-Carpio , B. R. Granett , A. Grazian , F. Grupp , S. V. H. Haugan , H. Hoekstra , W. Holmes , I. M. Hook , F. Hormuth , A. Hornstrup , P. Hudelot , K. Jahnke , M. Jhabvala , B. Joachimi , E. Keihänen , S. Kermiche , A. Kiessling , B. Kubik , M. Kümmel , M. Kunz , H. Kurki-Suonio , Q. Le Boulc'h , A. M. C. Le Brun , D. Le Mignant , 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 , S. Maurogordato , E. Medinaceli , S. Mei , Y. Mellier , M. Meneghetti , E. Merlin , G. Meylan , A. Mora , M. Moresco , L. Moscardini , R. Nakajima , C. Neissner , R. C. Nichol , S. -M. Niemi , 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 , R. Saglia , Z. Sakr , D. Sapone , B. Sartoris , J. A. Schewtschenko , M. Schirmer , P. Schneider , T. Schrabback , 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 , I. Tereno , N. Tessore , S. Toft , R. Toledo-Moreo , F. Torradeflot , 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 , M. Bolzonella , E. Bozzo , C. Burigana , R. Cabanac , A. Cappi , D. Di Ferdinando , J. A. Escartin Vigo , L. Gabarra , M. Huertas-Company , J. Martín-Fleitas , S. Matthew , N. Mauri , A. Pezzotta , M. Pöntinen , C. Porciani , I. Risso , V. Scottez , M. Sereno , M. Tenti , M. Viel , M. Wiesmann , Y. Akrami , S. Alvi , I. T. Andika , S. Anselmi , M. Archidiacono , F. Atrio-Barandela , K. Benson , P. Bergamini , D. Bertacca , M. Bethermin , A. Blanchard , L. Blot , M. L. Brown , S. Bruton , A. Calabro , F. Caro , C. S. Carvalho , T. Castro , F. Cogato , A. R. Cooray , O. Cucciati , S. Davini , F. De Paolis , G. Desprez , A. Díaz-Sánchez , J. J. Diaz , S. Di Domizio , J. M. Diego , 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 , G. Gozaliasl , M. Guidi , C. M. Gutierrez , A. Hall , W. G. Hartley , C. Hernández-Monteagudo , H. Hildebrandt , J. Hjorth , 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 , 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 , S. Nadathur , K. Naidoo , A. Navarro-Alsina , S. Nesseris , F. Passalacqua , K. Paterson , L. Patrizii , A. Pisani , D. Potter , S. Quai , M. Radovich , P. -F. Rocci , S. Sacquegna , M. Sahlén , D. B. Sanders , E. Sarpa , C. Scarlata , J. Schaye , A. Schneider , D. Sciotti , E. Sellentin , L. C. Smith , K. Tanidis , G. Testera , R. Teyssier , S. Tosi , A. Troja , M. Tucci , C. Valieri , A. Venhola , D. Vergani , G. Vernardos , G. Verza , P. Vielzeuf , N. A. Walton , J. Wilde , D. Scott

We report the discovery of new lensed quasar candidates in the imaging data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) DR4, covering $1\,310~{\rm deg}^2$ of the sky with seeing of $\approx0.6''$. In addition to two catalogs…

Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens systems with a high true-positive rate, the unbalanced…

We study the strong gravitational lensing properties of galaxy clusters obtained from N-body simulations with different kind of Dark Energy (DE). We consider both dynamical DE, due to a scalar field self--interacting through Ratra-Peebles…

天体物理学 · 物理学 2009-11-10 Andrea V. Maccio'

Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish to…

We present the results of a visual search for galaxy-scale gravitational lenses in nearly 7 square degrees of Hubble Space Telescope (HST) images. The dataset comprises the whole imaging data ever taken with the Advanced Camera for Surveys…

宇宙学与河外天体物理 · 物理学 2017-03-22 R. S. Pawase , C. Faure , F. Courbin , R. Kokotanekova , G. Meylan

We report on the first results of an imaging survey to detect strong gravitational lensing targeting the richest clusters selected from the photometric data of the Sloan Digital Sky Survey (SDSS) with follow-up deep imaging observations…