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Modeling the transfer function for the Dark Energy Survey

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Modeling the transfer function for the Dark Energy Survey

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Título Modeling the transfer function for the Dark Energy Survey
Autor 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.
Abstract 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.
Contido em The astrophysical journal. Bristol. Vol. 801, no. 2 (Mar. 2015), 73, 14 p.
Assunto Análise de dados
Cosmologia
Energia escura
Funções de transferência
Mapeamentos astronômicos
Processamento de imagen astronômica
[en] Methods: data analysis
[en] Methods: numerical
[en] Surveys
[en] Techniques: image processing
Origem Estrangeiro
Tipo Artigo de periódico
URI http://hdl.handle.net/10183/116522
Arquivos Descrição Formato
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