Photometric redshift analysis in the Dark Energy Survey Science Verification data

Journal Article · Thu Oct 09 04:00:00 UTC 2014 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stu1836 · OSTI ID:1374719

Sanchez, C.; Carrasco Kind, M.; Lin, H.; Miquel, R.; Abdalla, F. B.; Amara, A.; Banerji, M.; Bonnett, C.; Brunner, R.; Capozzi, D.; Carnero, A.; Castander, F. J.; da Costa, L. A. N.; Cunha, C.; Fausti, A.; Gerdes, D.; Greisel, N.; Gschwend, J.; Hartley, W.; Jouvel, S. ; Lahav, O.; Lima, M.; Maia, M. A. G.; Marti, P.; Ogando, R. L. C.; Ostrovski, F.; Pellegrini, P.; Rau, M. M.; Sadeh, I.; Seitz, S.; Sevilla-Noarbe, I.; Sypniewski, A.; de Vicente, J.; Abbot, T.; Allam, S. S.; Atlee, D.; Bernstein, G.; Bernstein, J. P.; Buckley-Geer, E.; Burke, D.; Childress, M. J.; Davis, T.; DePoy, D. L.; Dey, A.; Desai, S.; Diehl, H. T.; Doel, P.; Estrada, J.; Evrard, A.; Fernandez, E.; Finley, D.; Flaugher, B.; Frieman, J.; Gaztanaga, E.; Glazebrook, K.; Honscheid, K.; Kim, A.; Kuehn, K.; Kuropatkin, N.; Lidman, C.; Makler, M.; Marshall, J. L.; Nichol, R. C.; Roodman, A.; Sanchez, E.; Santiago, B. X.; Sako, M.; Scalzo, R.; Smith, R. C.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Tucker, D. L.; Uddin, S. A.; Valdes, F.; Walker, A.; Yuan, F.; Zuntz, J.

In this study, 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. In addition, 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.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• Contributing Organization: DES Collaboration
• Grant/Contract Number: AC02-07CH11359
• OSTI ID: 1374719
• Report Number(s): FERMILAB-PUB--14-260-AE; arXiv:1406.4407; 1300797
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 445; ISSN 0035-8711
• Publisher: Royal Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English