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The Dark Energy Survey (DES; operations 2009-2015) will address the nature of dark energy using four independent and complementary techniques: (1) a galaxy cluster survey over 4000 deg2 in collaboration with the South Pole Telescope…

The Dark Energy Survey (DES) is a 5000 deg2 grizY survey reaching characteristic photometric depths of 24th magnitude (10 sigma) and enabling accurate photometry and morphology of objects ten times fainter than in SDSS. Preparations for DES…

The Dark Energy Survey (DES) is a project with the goal of building, installing and exploiting a new 74 CCD-camera at the Blanco telescope, in order to study the nature of cosmic acceleration. It will cover 5000 square degrees of the…

We present the use of self-supervised learning to explore and exploit large unlabeled datasets. Focusing on 42 million galaxy images from the latest data release of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, we…

Instrumentation and Methods for Astrophysics · Physics 2021-12-02 George Stein , Peter Harrington , Jacqueline Blaum , Tomislav Medan , Zarija Lukic

The Dark Energy Survey is able to collect image data of an extremely large number of extragalactic objects, and it can be reasonably assumed that many unusual objects of high scientific interest are hidden inside these data. Due to the…

Astrophysics of Galaxies · Physics 2023-05-04 Lior Shamir

The Dark Energy Survey is a stage III dark energy experiment, performing an optical imaging survey to measure cosmological equation of state parameters using four independent methods. The scope and complexity of the survey introduced…

Instrumentation and Methods for Astrophysics · Physics 2019-12-16 Eric H. Neilsen , James T. Annis , H. Thomas Diehl , Molly E. C. Swanson , Chris D'Andrea , Stephen Kent , Alex Drlica-Wagner

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text,…

Human-Computer Interaction · Computer Science 2018-01-09 Florian Daniel , Pavel Kucherbaev , Cinzia Cappiello , Boualem Benatallah , Mohammad Allahbakhsh

The Dark Energy Survey (DES) is a 5000 sq deg griz imaging survey to be conducted using a proposed 3 sq deg (2.2deg-diameter) wide-field mosaic camera on the CTIO Blanco 4m telescope. The primary scientific goal of the DES is to constrain…

The Dark Energy Survey collaboration will study cosmic acceleration with a 5000 deg2 griZY survey in the southern sky over 525 nights from 2011-2016. The DES data management (DESDM) system will be used to process and archive these data and…

We describe the Dark Energy Survey (DES), a proposed optical-near infrared survey of 5000 sq. deg of the South Galactic Cap to ~24th magnitude in SDSS griz, that would use a new 3 sq. deg CCD camera to be mounted on the Blanco 4-m telescope…

Astrophysics · Physics 2012-08-27 The Dark Energy Survey Collaboration

We present a forward-modelling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function -- a mapping from cosmological and astronomical…

The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a 5000 square degree survey of the southern sky in five optical bands (g,r,i,z,Y) to a depth…

We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies to be used in weak gravitational lensing and galaxy clustering studies. To perform this measurement, we cross-correlate with spectroscopic…

We present a validation of the Dark Energy Survey Year 3 (DES Y3) $3\times2$-point analysis choices by testing them on Buzzard v2.0, a new suite of cosmological simulations that is tailored for the testing and validation of combined galaxy…

Cosmology and Nongalactic Astrophysics · Physics 2022-06-29 J. DeRose , R. H. Wechsler , M. R. Becker , E. S. Rykoff , S. Pandey , N. MacCrann , A. Amon , J. Myles , E. Krause , D. Gruen , B. Jain , M. A. Troxel , J. Prat , A. Alarcon , C. Sánchez , J. Blazek , M. Crocce , G. Giannini , M. Gatti , G. M. Bernstein , J. Zuntz , S. Dodelson , X. Fang , O. Friedrich , L. F. Secco , J. Elvin-Poole , S. Everett , A. Choi , I. Harrison , J. Cordero , M. Rodriguez-Monroy , J. McCullough , R. Cawthon , A. Chen , O. Alves , H. Camacho , A. Campos , H. T. Diehl , A. Drlica-Wagner , T. F. Eifler , P. Fosalba , H. Huang , A. Porredon , M. Raveri , R. Rosenfeld , A. J. Ross , J. Sanchez , E. Sheldon , B. Yanny , B. Yin , M. Aguena , S. Allam , F. Andrade-Oliveira , J. Annis , S. Avila , D. Bacon , K. Bechtol , S. Bhargava , D. Brooks , E. Buckley-Geer , D. L. Burke , A. Carnero Rosell , M. Carrasco Kind , C. Chang , M. Costanzi , L. N. da Costa , M. E. S. Pereira , J. De Vicente , S. Desai , J. P. Dietrich , P. Doel , K. Eckert , A. E. Evrard , I. Ferrero , A. Ferté , B. Flaugher , J. Frieman , J. García-Bellido , E. Gaztanaga , T. Giannantonio , R. A. Gruendl , J. Gschwend , G. Gutierrez , W. G. Hartley , S. R. Hinton , D. L. Hollowood , K. Honscheid , E. M. Huff , D. Huterer , D. J. James , K. Kuehn , N. Kuropatkin , O. Lahav , M. Lima , M. A. G. Maia , J. L. Marshall , P. Melchior , F. Menanteau , R. Miquel , J. J. Mohr , R. Morgan , A. Palmese , F. Paz-Chinchón , A. Pieres , A. A. Plazas Malagón , E. Sanchez , V. Scarpine , S. Serrano , I. Sevilla-Noarbe , M. Smith , M. Soares-Santos , E. Suchyta , G. Tarle , D. Thomas , C. To , T. N. Varga , Y. Zhang

The data that underlies automated methods in computer vision and machine learning, such as image retrieval and fine-grained recognition, often comes from crowdsourcing. In contexts that rely on the intrinsic motivation of users, we seek to…

Human-Computer Interaction · Computer Science 2024-09-06 Abby Stylianou , Michelle Brachman , Albatool Wazzan , Samuel Black , Richard Souvenir

We describe the model for the mapping from sky brightness to the digital output of the Dark Energy Camera, and the algorithms adopted by the Dark Energy Survey (DES) for inverting this model to obtain photometric measures of celestial…

Instrumentation and Methods for Astrophysics · Physics 2017-09-20 G. M. Bernstein , T. M. C. Abbott , S. Desai , D. Gruen , R. A. Gruendl , M. D. Johnson , H. Lin , F. Menanteau , E. Morganson , E. Neilsen , K. Paech , A. R. Walker , W. Wester , B. Yanny

We present the methodology for the weak lensing and galaxy clustering analyses of the Dark Energy Survey (DES) Year 6 data set. In this work, we design and validate the analysis pipeline for the cosmic shear, galaxy clustering plus…

Cosmology and Nongalactic Astrophysics · Physics 2026-01-22 D. Sanchez-Cid , A. Ferté , J. Blazek , S. Samuroff , A. Amon , F. Andrade-Oliveira , J. M. Coloma-Nadal , J. Muir , A. Porredon , J. Prat , N. Weaverdyck , M. Yamamoto , D. Anbajagane , M. R. Becker , P. Carrilho , C. Chang , M. Crocce , G. Giannini , W. d'Assignies , J. DeRose , S. Dodelson , E. Krause , E. Legnani , J. Mena-Fernández , N. MacCrann , A. Pourtsidou , C. Preston , P. Rogozenski , M. Rodriguez-Monroy , R. Rosenfeld , E. Sanchez , I. Sevilla-Noarbe , M. Soares-Santos , C. To , M. A. Troxel , M. Tsedrik , B. Yin , J. Zuntz , T. M. C. Abbott , M. Aguena , S. Allam , O. Alves , S. Avila , D. Bacon , K. Bechtol , E. Bertin , S. Bocquet , D. Brooks , H. Camacho , R. Camilleri , A. Campos , A. Carnero Rosell , J. Carretero , F. J. Castander , R. Cawthon , A. Choi , L. N. da Costa , M. E. da Silva Pereira , T. M. Davis , J. De Vicente , S. Desai , C. Doux , A. Drlica-Wagner , T. Eifler , J. Elvin-Poole , S. Everett , A. E. Evrard , B. Flaugher , P. Fosalba , J. Frieman , J. García-Bellido , M. Gatti , E. Gaztanaga , P. Giles , K. Glazebrook , D. Gruen , G. Gutierrez , I. Harrison , K. Herner , S. R. Hinton , D. L. Hollowood , K. Honscheid , D. Huterer , B. Jain , D. J. James , N. Jeffrey , T. Kacprzak , K. Kuehn , O. Lahav , S. Lee , J. L. Marshall , F. Menanteau , R. Miquel , J. J. Mohr , J. Myles , R. C. Nichol , R. L. C. Ogando , A. Palmese , M. Paterno , W. J. Percival , A. A. Plazas Malagón , M. Raveri , A. Roodman , C. Sánchez , T. Schutt , E. Sheldon , N. Sherman , T. Shin , M. Smith , E. Suchyta , M. E. C. Swanson , M. Tabbutt , G. Tarle , D. Thomas , D. L. Tucker , V. Vikram , A. R. Walker , B. Yanny
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