Photometric redshift analysis in the Dark Energy Survey science verification data

Detalhes bibliográficos
Autor(a) principal: Sánchez Alonso, Carles
Data de Publicação: 2014
Outros Autores: Carrasco Kind, Matías, Lin, H., Serra Ricart, Miquel, Abdalla, Filipe B., Amara, Adam, Banerji, M., Bonnet, Christopher Clive, Brunner, Robert J., Capozzi, Diego, Carnero Rosell, Aurelio, Castander Serentill, Francisco Javier, Costa, Luiz N. da, Cunha, Carlos Eduardo, Fausti Neto, Angelo, Gerdes, David W., Greisel, N., Gschwend, Julia de Figueiredo, Hartley, William, Jouvel, Stéphanie, Lahav, Ofer, Lima, Marcos Vinicius Borges Teixeira, Maia, Marcio Antonio Geimba, Martí, P., Ogando, Ricardo L.C., Ostrovski, F., Pellegrini, Paulo Sérgio de Souza, Rau, Markus Michael, Sadeh, Iftach, Seitz, Stella, Sevilla Noarbe, Ignacio, Sypniewski, Adam J., Vicente, J. de, Abbott, Timothy M. C., Allam, Sahar S., Atlee, David, Bernstein, Gary M., Bernstein, Joseph P., Buckley-Geer, Elizabeth, Burke, David Lyle, Childress, Michael, Davis, Tamara M., DePoy, Darren L., Dey, Arjun, Desai, S., Diehl, H. Thomas, Doel, Peter, Estrada, Juan, Evrard, August E., Fernandez, Enrique, Finley, David A., Flaugher, Brenna, Frieman, Joshua A., Gaztañaga, Enrique, Glazebrook, Karl, Honscheid, K., Kim, A. G., Kuehn, Kyler, Kuropatkin, Nikolay P., Lidman, Chris, Makler, Martín, Marshall, Jennifer L., Nichol, Robert C., Roodman, Aaron, Sanchez-Alvaro, Eusebio, Santiago, Basilio Xavier, Sako, Masao, Scalzo, Richard, Smith, Robert Christopher, Swanson, Molly E. C., Tarle, Gregory, Thomas, D., Tucker, Douglas L., Uddin, Syed, Valdes, Francisco, Walker, Alistair, Yuan, Fang, Zuntz, J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/115354
Resumo: We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq. deg. at the nominal depth of the survey.We assess the photometric redshift (photo-z) performance using about 15 000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-z’s are obtained and studied using most of the existing photo-z codes. A weighting method in a multidimensional colour–magnitude space is applied to the spectroscopic sample in order to evaluate the photo-z performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. Empirical photo-z methods using, for instance, artificial neural networks or random forests, yield the best performance in the tests, achieving core photo-z resolutions σ68 ∼ 0.08. Moreover, the results from most of the codes, including template-fitting methods, comfortably meet the DES requirements on photo-z performance, therefore, providing an excellent precedent for future DES data sets.
id UFRGS-2_c8fdda90765ce34e0516e15ffafac85f
oai_identifier_str oai:www.lume.ufrgs.br:10183/115354
network_acronym_str UFRGS-2
network_name_str Repositório Institucional da UFRGS
repository_id_str
spelling Sánchez Alonso, CarlesCarrasco Kind, MatíasLin, H.Serra Ricart, MiquelAbdalla, Filipe B.Amara, AdamBanerji, M.Bonnet, Christopher CliveBrunner, Robert J.Capozzi, DiegoCarnero Rosell, AurelioCastander Serentill, Francisco JavierCosta, Luiz N. daCunha, Carlos EduardoFausti Neto, AngeloGerdes, David W.Greisel, N.Gschwend, Julia de FigueiredoHartley, WilliamJouvel, StéphanieLahav, OferLima, Marcos Vinicius Borges TeixeiraMaia, Marcio Antonio GeimbaMartí, P.Ogando, Ricardo L.C.Ostrovski, F.Pellegrini, Paulo Sérgio de SouzaRau, Markus MichaelSadeh, IftachSeitz, StellaSevilla Noarbe, IgnacioSypniewski, Adam J.Vicente, J. deAbbott, Timothy M. C.Allam, Sahar S.Atlee, DavidBernstein, Gary M.Bernstein, Joseph P.Buckley-Geer, ElizabethBurke, David LyleChildress, MichaelDavis, Tamara M.DePoy, Darren L.Dey, ArjunDesai, S.Diehl, H. ThomasDoel, PeterEstrada, JuanEvrard, August E.Fernandez, EnriqueFinley, David A.Flaugher, BrennaFrieman, Joshua A.Gaztañaga, EnriqueGlazebrook, KarlHonscheid, K.Kim, A. G.Kuehn, KylerKuropatkin, Nikolay P.Lidman, ChrisMakler, MartínMarshall, Jennifer L.Nichol, Robert C.Roodman, AaronSanchez-Alvaro, EusebioSantiago, Basilio XavierSako, MasaoScalzo, RichardSmith, Robert ChristopherSwanson, Molly E. C.Tarle, GregoryThomas, D.Tucker, Douglas L.Uddin, SyedValdes, FranciscoWalker, AlistairYuan, FangZuntz, J.2015-04-15T01:58:21Z20140035-8711http://hdl.handle.net/10183/115354000963587We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq. deg. at the nominal depth of the survey.We assess the photometric redshift (photo-z) performance using about 15 000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-z’s are obtained and studied using most of the existing photo-z codes. A weighting method in a multidimensional colour–magnitude space is applied to the spectroscopic sample in order to evaluate the photo-z performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. Empirical photo-z methods using, for instance, artificial neural networks or random forests, yield the best performance in the tests, achieving core photo-z resolutions σ68 ∼ 0.08. Moreover, the results from most of the codes, including template-fitting methods, comfortably meet the DES requirements on photo-z performance, therefore, providing an excellent precedent for future DES data sets.application/pdfengMonthly notices of the Royal Astronomical Society. Oxford. Vol. 445, no. 2 (Dec. 2014), p. 1482-1506Mapeamentos astronômicosAstronomical data bases: surveysGalaxies: distances and redshiftsGalaxies: statisticsLarge-scale structure of UniversePhotometric redshift analysis in the Dark Energy Survey science verification dataEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000963587.pdf.txt000963587.pdf.txtExtracted Texttext/plain130439http://www.lume.ufrgs.br/bitstream/10183/115354/2/000963587.pdf.txta985d63023161fa62a5ed067a4a84acdMD52ORIGINAL000963587.pdf000963587.pdfTexto completo (inglês)application/pdf2832409http://www.lume.ufrgs.br/bitstream/10183/115354/1/000963587.pdf2f50c2a6a57539aea3b0b3c2f818c00cMD51THUMBNAIL000963587.pdf.jpg000963587.pdf.jpgGenerated Thumbnailimage/jpeg2175http://www.lume.ufrgs.br/bitstream/10183/115354/3/000963587.pdf.jpgb522bdad4ceb91682767f9209e845870MD5310183/1153542023-07-02 03:41:59.967222oai:www.lume.ufrgs.br:10183/115354Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-02T06:41:59Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Photometric redshift analysis in the Dark Energy Survey science verification data
title Photometric redshift analysis in the Dark Energy Survey science verification data
spellingShingle Photometric redshift analysis in the Dark Energy Survey science verification data
Sánchez Alonso, Carles
Mapeamentos astronômicos
Astronomical data bases: surveys
Galaxies: distances and redshifts
Galaxies: statistics
Large-scale structure of Universe
title_short Photometric redshift analysis in the Dark Energy Survey science verification data
title_full Photometric redshift analysis in the Dark Energy Survey science verification data
title_fullStr Photometric redshift analysis in the Dark Energy Survey science verification data
title_full_unstemmed Photometric redshift analysis in the Dark Energy Survey science verification data
title_sort Photometric redshift analysis in the Dark Energy Survey science verification data
author Sánchez Alonso, Carles
author_facet Sánchez Alonso, Carles
Carrasco Kind, Matías
Lin, H.
Serra Ricart, Miquel
Abdalla, Filipe B.
Amara, Adam
Banerji, M.
Bonnet, Christopher Clive
Brunner, Robert J.
Capozzi, Diego
Carnero Rosell, Aurelio
Castander Serentill, Francisco Javier
Costa, Luiz N. da
Cunha, Carlos Eduardo
Fausti Neto, Angelo
Gerdes, David W.
Greisel, N.
Gschwend, Julia de Figueiredo
Hartley, William
Jouvel, Stéphanie
Lahav, Ofer
Lima, Marcos Vinicius Borges Teixeira
Maia, Marcio Antonio Geimba
Martí, P.
Ogando, Ricardo L.C.
Ostrovski, F.
Pellegrini, Paulo Sérgio de Souza
Rau, Markus Michael
Sadeh, Iftach
Seitz, Stella
Sevilla Noarbe, Ignacio
Sypniewski, Adam J.
Vicente, J. de
Abbott, Timothy M. C.
Allam, Sahar S.
Atlee, David
Bernstein, Gary M.
Bernstein, Joseph P.
Buckley-Geer, Elizabeth
Burke, David Lyle
Childress, Michael
Davis, Tamara M.
DePoy, Darren L.
Dey, Arjun
Desai, S.
Diehl, H. Thomas
Doel, Peter
Estrada, Juan
Evrard, August E.
Fernandez, Enrique
Finley, David A.
Flaugher, Brenna
Frieman, Joshua A.
Gaztañaga, Enrique
Glazebrook, Karl
Honscheid, K.
Kim, A. G.
Kuehn, Kyler
Kuropatkin, Nikolay P.
Lidman, Chris
Makler, Martín
Marshall, Jennifer L.
Nichol, Robert C.
Roodman, Aaron
Sanchez-Alvaro, Eusebio
Santiago, Basilio Xavier
Sako, Masao
Scalzo, Richard
Smith, Robert Christopher
Swanson, Molly E. C.
Tarle, Gregory
Thomas, D.
Tucker, Douglas L.
Uddin, Syed
Valdes, Francisco
Walker, Alistair
Yuan, Fang
Zuntz, J.
author_role author
author2 Carrasco Kind, Matías
Lin, H.
Serra Ricart, Miquel
Abdalla, Filipe B.
Amara, Adam
Banerji, M.
Bonnet, Christopher Clive
Brunner, Robert J.
Capozzi, Diego
Carnero Rosell, Aurelio
Castander Serentill, Francisco Javier
Costa, Luiz N. da
Cunha, Carlos Eduardo
Fausti Neto, Angelo
Gerdes, David W.
Greisel, N.
Gschwend, Julia de Figueiredo
Hartley, William
Jouvel, Stéphanie
Lahav, Ofer
Lima, Marcos Vinicius Borges Teixeira
Maia, Marcio Antonio Geimba
Martí, P.
Ogando, Ricardo L.C.
Ostrovski, F.
Pellegrini, Paulo Sérgio de Souza
Rau, Markus Michael
Sadeh, Iftach
Seitz, Stella
Sevilla Noarbe, Ignacio
Sypniewski, Adam J.
Vicente, J. de
Abbott, Timothy M. C.
Allam, Sahar S.
Atlee, David
Bernstein, Gary M.
Bernstein, Joseph P.
Buckley-Geer, Elizabeth
Burke, David Lyle
Childress, Michael
Davis, Tamara M.
DePoy, Darren L.
Dey, Arjun
Desai, S.
Diehl, H. Thomas
Doel, Peter
Estrada, Juan
Evrard, August E.
Fernandez, Enrique
Finley, David A.
Flaugher, Brenna
Frieman, Joshua A.
Gaztañaga, Enrique
Glazebrook, Karl
Honscheid, K.
Kim, A. G.
Kuehn, Kyler
Kuropatkin, Nikolay P.
Lidman, Chris
Makler, Martín
Marshall, Jennifer L.
Nichol, Robert C.
Roodman, Aaron
Sanchez-Alvaro, Eusebio
Santiago, Basilio Xavier
Sako, Masao
Scalzo, Richard
Smith, Robert Christopher
Swanson, Molly E. C.
Tarle, Gregory
Thomas, D.
Tucker, Douglas L.
Uddin, Syed
Valdes, Francisco
Walker, Alistair
Yuan, Fang
Zuntz, J.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Sánchez Alonso, Carles
Carrasco Kind, Matías
Lin, H.
Serra Ricart, Miquel
Abdalla, Filipe B.
Amara, Adam
Banerji, M.
Bonnet, Christopher Clive
Brunner, Robert J.
Capozzi, Diego
Carnero Rosell, Aurelio
Castander Serentill, Francisco Javier
Costa, Luiz N. da
Cunha, Carlos Eduardo
Fausti Neto, Angelo
Gerdes, David W.
Greisel, N.
Gschwend, Julia de Figueiredo
Hartley, William
Jouvel, Stéphanie
Lahav, Ofer
Lima, Marcos Vinicius Borges Teixeira
Maia, Marcio Antonio Geimba
Martí, P.
Ogando, Ricardo L.C.
Ostrovski, F.
Pellegrini, Paulo Sérgio de Souza
Rau, Markus Michael
Sadeh, Iftach
Seitz, Stella
Sevilla Noarbe, Ignacio
Sypniewski, Adam J.
Vicente, J. de
Abbott, Timothy M. C.
Allam, Sahar S.
Atlee, David
Bernstein, Gary M.
Bernstein, Joseph P.
Buckley-Geer, Elizabeth
Burke, David Lyle
Childress, Michael
Davis, Tamara M.
DePoy, Darren L.
Dey, Arjun
Desai, S.
Diehl, H. Thomas
Doel, Peter
Estrada, Juan
Evrard, August E.
Fernandez, Enrique
Finley, David A.
Flaugher, Brenna
Frieman, Joshua A.
Gaztañaga, Enrique
Glazebrook, Karl
Honscheid, K.
Kim, A. G.
Kuehn, Kyler
Kuropatkin, Nikolay P.
Lidman, Chris
Makler, Martín
Marshall, Jennifer L.
Nichol, Robert C.
Roodman, Aaron
Sanchez-Alvaro, Eusebio
Santiago, Basilio Xavier
Sako, Masao
Scalzo, Richard
Smith, Robert Christopher
Swanson, Molly E. C.
Tarle, Gregory
Thomas, D.
Tucker, Douglas L.
Uddin, Syed
Valdes, Francisco
Walker, Alistair
Yuan, Fang
Zuntz, J.
dc.subject.por.fl_str_mv Mapeamentos astronômicos
topic Mapeamentos astronômicos
Astronomical data bases: surveys
Galaxies: distances and redshifts
Galaxies: statistics
Large-scale structure of Universe
dc.subject.eng.fl_str_mv Astronomical data bases: surveys
Galaxies: distances and redshifts
Galaxies: statistics
Large-scale structure of Universe
description We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq. deg. at the nominal depth of the survey.We assess the photometric redshift (photo-z) performance using about 15 000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-z’s are obtained and studied using most of the existing photo-z codes. A weighting method in a multidimensional colour–magnitude space is applied to the spectroscopic sample in order to evaluate the photo-z performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. Empirical photo-z methods using, for instance, artificial neural networks or random forests, yield the best performance in the tests, achieving core photo-z resolutions σ68 ∼ 0.08. Moreover, the results from most of the codes, including template-fitting methods, comfortably meet the DES requirements on photo-z performance, therefore, providing an excellent precedent for future DES data sets.
publishDate 2014
dc.date.issued.fl_str_mv 2014
dc.date.accessioned.fl_str_mv 2015-04-15T01:58:21Z
dc.type.driver.fl_str_mv Estrangeiro
info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/115354
dc.identifier.issn.pt_BR.fl_str_mv 0035-8711
dc.identifier.nrb.pt_BR.fl_str_mv 000963587
identifier_str_mv 0035-8711
000963587
url http://hdl.handle.net/10183/115354
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Monthly notices of the Royal Astronomical Society. Oxford. Vol. 445, no. 2 (Dec. 2014), p. 1482-1506
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRGS
instname:Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
instname_str Universidade Federal do Rio Grande do Sul (UFRGS)
instacron_str UFRGS
institution UFRGS
reponame_str Repositório Institucional da UFRGS
collection Repositório Institucional da UFRGS
bitstream.url.fl_str_mv http://www.lume.ufrgs.br/bitstream/10183/115354/2/000963587.pdf.txt
http://www.lume.ufrgs.br/bitstream/10183/115354/1/000963587.pdf
http://www.lume.ufrgs.br/bitstream/10183/115354/3/000963587.pdf.jpg
bitstream.checksum.fl_str_mv a985d63023161fa62a5ed067a4a84acd
2f50c2a6a57539aea3b0b3c2f818c00c
b522bdad4ceb91682767f9209e845870
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)
repository.mail.fl_str_mv
_version_ 1815447579982299136