Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization
Publisher
OXFORD UNIV PRESSCitation
M Gatti, P Vielzeuf, C Davis, R Cawthon, M M Rau, J DeRose, J De Vicente, A Alarcon, E Rozo, E Gaztanaga, B Hoyle, R Miquel, G M Bernstein, C Bonnett, A Carnero Rosell, F J Castander, C Chang, L N da Costa, D Gruen, J Gschwend, W G Hartley, H Lin, N MacCrann, M A G Maia, R L C Ogando, A Roodman, I Sevilla-Noarbe, M A Troxel, R H Wechsler, J Asorey, T M Davis, K Glazebrook, S R Hinton, G Lewis, C Lidman, E Macaulay, A Möller, C R O'Neill, N E Sommer, S A Uddin, F Yuan, B Zhang, T M C Abbott, S Allam, J Annis, K Bechtol, D Brooks, D L Burke, D Carollo, M Carrasco Kind, J Carretero, C E Cunha, C B D'Andrea, D L DePoy, S Desai, T F Eifler, A E Evrard, B Flaugher, P Fosalba, J Frieman, J García-Bellido, D W Gerdes, D A Goldstein, R A Gruendl, G Gutierrez, K Honscheid, J K Hoormann, B Jain, D J James, M Jarvis, T Jeltema, M W G Johnson, M D Johnson, E Krause, K Kuehn, S Kuhlmann, N Kuropatkin, T S Li, M Lima, J L Marshall, P Melchior, F Menanteau, R C Nichol, B Nord, A A Plazas, K Reil, E S Rykoff, M Sako, E Sanchez, V Scarpine, M Schubnell, E Sheldon, M Smith, R C Smith, M Soares-Santos, F Sobreira, E Suchyta, M E C Swanson, G Tarle, D Thomas, B E Tucker, D L Tucker, V Vikram, A R Walker, J Weller, W Wester, R C Wolf; Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization, Monthly Notices of the Royal Astronomical Society, Volume 477, Issue 2, 21 June 2018, Pages 1664–1682, https://doi.org/10.1093/mnras/sty466Rights
© 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical SocietyCollection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Delta z less than or similar to 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.ISSN
0035-87111365-2966