Modeling the transfer function for the Dark Energy Survey
Autor(a) principal: | |
---|---|
Data de Publicação: | 2015 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/116522 |
Resumo: | We present a forward-modeling 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—amapping from cosmological/ astronomical signals to the final data products used by the scientists.Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg2 coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured.We also point out several directions of future improvements. Two practical examples—star–galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable. |
id |
UFRGS-2_9f687db3796c8571b7adc77f8072d00a |
---|---|
oai_identifier_str |
oai:www.lume.ufrgs.br:10183/116522 |
network_acronym_str |
UFRGS-2 |
network_name_str |
Repositório Institucional da UFRGS |
repository_id_str |
|
spelling |
Chang, ChihwayBusha, M. T.Wechsler, Risa H.Refregier, AlexandreAmara, AdamRykoff, EliBecker, Matthew R.Bruderer, ClaudioGamper, L.Leistedt, BorisPeiris, Hiranya V.Abbott, Timothy M. C.Abdalla, Filipe B.Balbinot, EduardoBanerji, M.Bernstein, Rebecca A.Bertin, EmmanuelBrooks, D.Carnero Rosell, AurelioDesai, S.Costa, Luiz N. daCunha, Carlos EduardoEifler, TimEvrard, August E.Fausti Neto, AngeloGerdes, David W.Gruen, DanielJames, David J.Kuehn, KylerMaia, Marcio Antonio GeimbaMakler, MartínOgando, Ricardo L.C.Plazas Malagón, Andrés AlejandroSanchez-Alvaro, EusebioSantiago, Basilio XavierSchubnell, MichaelSevilla Noarbe, IgnacioSmith, Robert ChristopherSoares-Santos, MarcelleSuchyta, EricSwanson, Molly E. C.Tarle, GregoryZuntz, J.2015-05-16T02:00:44Z20150004-637Xhttp://hdl.handle.net/10183/116522000965571We present a forward-modeling 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—amapping from cosmological/ astronomical signals to the final data products used by the scientists.Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg2 coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured.We also point out several directions of future improvements. Two practical examples—star–galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable.application/pdfengThe astrophysical journal. Bristol. Vol. 801, no. 2 (Mar. 2015), 73, 14 p.CosmologiaEnergia escuraAnálise de dadosFunções de transferênciaMapeamentos astronômicosProcessamento de imagensMethods: data analysisMethods: numericalSurveysTechniques: image processingModeling the transfer function for the Dark Energy SurveyEstrangeiroinfo: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:UFRGSTEXT000965571.pdf.txt000965571.pdf.txtExtracted Texttext/plain73693http://www.lume.ufrgs.br/bitstream/10183/116522/2/000965571.pdf.txt81adc5eada33093c8db75dfc76de35adMD52ORIGINAL000965571.pdf000965571.pdfTexto completo (inglês)application/pdf2042211http://www.lume.ufrgs.br/bitstream/10183/116522/1/000965571.pdf4220f96084780ab4da5db43a10bb59fdMD51THUMBNAIL000965571.pdf.jpg000965571.pdf.jpgGenerated Thumbnailimage/jpeg1897http://www.lume.ufrgs.br/bitstream/10183/116522/3/000965571.pdf.jpg6f2435f0bbd288af1c53bdfe7721a7bfMD5310183/1165222023-07-02 03:41:34.959646oai:www.lume.ufrgs.br:10183/116522Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-02T06:41:34Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Modeling the transfer function for the Dark Energy Survey |
title |
Modeling the transfer function for the Dark Energy Survey |
spellingShingle |
Modeling the transfer function for the Dark Energy Survey Chang, Chihway Cosmologia Energia escura Análise de dados Funções de transferência Mapeamentos astronômicos Processamento de imagens Methods: data analysis Methods: numerical Surveys Techniques: image processing |
title_short |
Modeling the transfer function for the Dark Energy Survey |
title_full |
Modeling the transfer function for the Dark Energy Survey |
title_fullStr |
Modeling the transfer function for the Dark Energy Survey |
title_full_unstemmed |
Modeling the transfer function for the Dark Energy Survey |
title_sort |
Modeling the transfer function for the Dark Energy Survey |
author |
Chang, Chihway |
author_facet |
Chang, Chihway Busha, M. T. Wechsler, Risa H. Refregier, Alexandre Amara, Adam Rykoff, Eli Becker, Matthew R. Bruderer, Claudio Gamper, L. Leistedt, Boris Peiris, Hiranya V. Abbott, Timothy M. C. Abdalla, Filipe B. Balbinot, Eduardo Banerji, M. Bernstein, Rebecca A. Bertin, Emmanuel Brooks, D. Carnero Rosell, Aurelio Desai, S. Costa, Luiz N. da Cunha, Carlos Eduardo Eifler, Tim Evrard, August E. Fausti Neto, Angelo Gerdes, David W. Gruen, Daniel James, David J. Kuehn, Kyler Maia, Marcio Antonio Geimba Makler, Martín Ogando, Ricardo L.C. Plazas Malagón, Andrés Alejandro Sanchez-Alvaro, Eusebio Santiago, Basilio Xavier Schubnell, Michael Sevilla Noarbe, Ignacio Smith, Robert Christopher Soares-Santos, Marcelle Suchyta, Eric Swanson, Molly E. C. Tarle, Gregory Zuntz, J. |
author_role |
author |
author2 |
Busha, M. T. Wechsler, Risa H. Refregier, Alexandre Amara, Adam Rykoff, Eli Becker, Matthew R. Bruderer, Claudio Gamper, L. Leistedt, Boris Peiris, Hiranya V. Abbott, Timothy M. C. Abdalla, Filipe B. Balbinot, Eduardo Banerji, M. Bernstein, Rebecca A. Bertin, Emmanuel Brooks, D. Carnero Rosell, Aurelio Desai, S. Costa, Luiz N. da Cunha, Carlos Eduardo Eifler, Tim Evrard, August E. Fausti Neto, Angelo Gerdes, David W. Gruen, Daniel James, David J. Kuehn, Kyler Maia, Marcio Antonio Geimba Makler, Martín Ogando, Ricardo L.C. Plazas Malagón, Andrés Alejandro Sanchez-Alvaro, Eusebio Santiago, Basilio Xavier Schubnell, Michael Sevilla Noarbe, Ignacio Smith, Robert Christopher Soares-Santos, Marcelle Suchyta, Eric Swanson, Molly E. C. Tarle, Gregory 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 |
dc.contributor.author.fl_str_mv |
Chang, Chihway Busha, M. T. Wechsler, Risa H. Refregier, Alexandre Amara, Adam Rykoff, Eli Becker, Matthew R. Bruderer, Claudio Gamper, L. Leistedt, Boris Peiris, Hiranya V. Abbott, Timothy M. C. Abdalla, Filipe B. Balbinot, Eduardo Banerji, M. Bernstein, Rebecca A. Bertin, Emmanuel Brooks, D. Carnero Rosell, Aurelio Desai, S. Costa, Luiz N. da Cunha, Carlos Eduardo Eifler, Tim Evrard, August E. Fausti Neto, Angelo Gerdes, David W. Gruen, Daniel James, David J. Kuehn, Kyler Maia, Marcio Antonio Geimba Makler, Martín Ogando, Ricardo L.C. Plazas Malagón, Andrés Alejandro Sanchez-Alvaro, Eusebio Santiago, Basilio Xavier Schubnell, Michael Sevilla Noarbe, Ignacio Smith, Robert Christopher Soares-Santos, Marcelle Suchyta, Eric Swanson, Molly E. C. Tarle, Gregory Zuntz, J. |
dc.subject.por.fl_str_mv |
Cosmologia Energia escura Análise de dados Funções de transferência Mapeamentos astronômicos Processamento de imagens |
topic |
Cosmologia Energia escura Análise de dados Funções de transferência Mapeamentos astronômicos Processamento de imagens Methods: data analysis Methods: numerical Surveys Techniques: image processing |
dc.subject.eng.fl_str_mv |
Methods: data analysis Methods: numerical Surveys Techniques: image processing |
description |
We present a forward-modeling 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—amapping from cosmological/ astronomical signals to the final data products used by the scientists.Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg2 coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured.We also point out several directions of future improvements. Two practical examples—star–galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable. |
publishDate |
2015 |
dc.date.accessioned.fl_str_mv |
2015-05-16T02:00:44Z |
dc.date.issued.fl_str_mv |
2015 |
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/10183/116522 |
dc.identifier.issn.pt_BR.fl_str_mv |
0004-637X |
dc.identifier.nrb.pt_BR.fl_str_mv |
000965571 |
identifier_str_mv |
0004-637X 000965571 |
url |
http://hdl.handle.net/10183/116522 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
The astrophysical journal. Bristol. Vol. 801, no. 2 (Mar. 2015), 73, 14 p. |
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/10183/116522/2/000965571.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/116522/1/000965571.pdf http://www.lume.ufrgs.br/bitstream/10183/116522/3/000965571.pdf.jpg |
bitstream.checksum.fl_str_mv |
81adc5eada33093c8db75dfc76de35ad 4220f96084780ab4da5db43a10bb59fd 6f2435f0bbd288af1c53bdfe7721a7bf |
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_ |
1801224870903676928 |