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DES Science Portal: I - Computing Photometric Redshifts | Julia Gschwend
; Aurelio Carnero Rosell
; Ricardo Ogando
; Angelo Fausti Neto
; Marcio Maia
; Luiz da Costa
; Marcos Lima
; Paulo Pellegrini
; Riccardo Campisano
; Cristiano Singulani
; Carlos de Souza
; Matias Carrasco Kind
; Tamara Davis
; Juan de Vicente
; Will Hartley
; Ben Hoyle
; Antonella Palmese
; Michael Markus Rau
; Iftach Sadeh
; Filipe Abdalla
; Sahar Allam
; Jacobo Asorey
; Aurélien Benoit-Lévy
; Emmanuel Bertin
; David Brooks
; Elizabeth Buckley-Geer
; Jorge Carretero
; Francisco Castander
; Carlos Cunha
; Chris D'Andrea
; Darren DePoy
; Shantanu Desai
; Peter Doel
; Tim Eifler
; August Evrard
; Pablo Fosalba
; Josh Frieman
; Juan Garcia-Bellido
; Enrique Gaztanaga
; Tommaso Giannantonio
; Karl Glazebrook
; Robert Gruendl
; Gaston Gutierrez
; Samuel Hinton
; Janie Hoormann
; David James
; Anthea King
; Kyler Kuehn
; Nikolay Kuropatkin
; Ofer Lahav
; Geraint Lewis
; Chris Lidman
; Edward Macaulay
; Marisa March
; Jennifer Marshall
; Paul Martini
; Felipe Menanteau
; Ramon Miquel
; Anais Moller
; Dale Mudd
; Andrés Plazas Malagón
; Kathy Romer
; Eusebio Sanchez
; Basilio Santiago
; Vic Scarpine
; Ignacio Sevilla
; Rob Sharp
; Mathew Smith
; Marcelle Soares-Santos
; Flavia Sobreira
; Natalia Eiré Sommer
; Eric Suchyta
; Molly Swanson
; Douglas Tucker
; Brad Tucker
; Syed Uddin
; Alistair Walker
; Bonnie Zhang
; | Date: |
18 Aug 2017 | Abstract: | We present the Dark Energy Survey (DES) Science Portal, an integrated
web-based data interface designed to facilitate scientific analysis. We
demonstrate how the Portal can provide a reliable environment to access
complete data sets, provide validation algorithms and metrics in the case of
multiple methods and training configurations, and maintain the provenance
between the different steps of a complex calculation, while ensuring
reproducibility of the results. We use the estimation of DES photometric
redshifts (photo-$z$s) as an example. A significant challenge facing
photometric surveys for cosmological purposes, such as DES, is the need to
produce reliable redshift estimates. The choice between competing algorithms
and configurations and the maintenance of an up-to-date spectroscopic database
to build training sets, for example, are complex tasks when dealing with large
amounts of data that are regularly updated and constantly growing. We show how
the DES Science Portal can be used to train and validate several photo-$z$
algorithms using the DES first year (Y1A1) data. The photo-$z$s estimated in
the Portal are used to feed the creation of catalogs for scientific workflows.
While the DES collaboration is still developing techniques to obtain precise
photo-$z$s, having a structured framework like the one presented here is
critical for the systematic vetting of DES algorithmic improvements and the
consistent production of photo-$z$s in future DES releases. | Source: | arXiv, 1708.5643 | Services: | Forum | Review | PDF | Favorites |
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