Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization
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 (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) 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- methods applied to the same source galaxy sample. We apply the method to three photo- codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo- (RF). 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 vs photo-'s. The systematic uncertainty in the mean redshift bias of the source galaxy sample is , 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.
Keywords
Cite
@article{arxiv.1709.00992,
title = {Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization},
author = {M. Gatti and P. Vielzeuf and C. Davis and R. Cawthon and M. M. Rau and J. DeRose and J. De Vicente and A. Alarcon and E. Rozo and E. Gaztanaga and B. Hoyle and R. Miquel and G. M. Bernstein and C. Bonnett and A. Carnero Rosell and F. J. Castander and C. Chang and L. N. da Costa and D. Gruen and J. Gschwend and W. G. Hartley and H. Lin and N. MacCrann and M. A. G. Maia and R. L. C. Ogando and A. Roodman and I. Sevilla-Noarbe and M. A. Troxel and R. H. Wechsler and J. Asorey and T. M. Davis and K. Glazebrook and S. R. Hinton and G. Lewis and C. Lidman and E. Macaulay and A. Möller and C. R. O'Neill and N. E. Sommer and S. A. Uddin and F. Yuan and B. Zhang and T. M. C. Abbott and S. Allam and J. Annis and K. Bechtol and D. Brooks and D. L. Burke and D. Carollo and M. Carrasco Kind and J. Carretero and C. E. Cunha and C. B. D'Andrea and D. L. DePoy and S. Desai and T. F. Eifler and A. E. Evrard and B. Flaugher and P. Fosalba and J. Frieman and J. García-Bellido and D. W. Gerdes and D. A. Goldstein and R. A. Gruendl and G. Gutierrez and K. Honscheid and J. K. Hoormann and B. Jain and D. J. James and M. Jarvis and T. Jeltema and M. W. G. Johnson and M. D. Johnson and E. Krause and K. Kuehn and S. Kuhlmann and N. Kuropatkin and T. S. Li and M. Lima and J. L. Marshall and P. Melchior and F. Menanteau and R. C. Nichol and B. Nord and A. A. Plazas and K. Reil and E. S. Rykoff and M. Sako and E. Sanchez and V. Scarpine and M. Schubnell and E. Sheldon and M. Smith and R. C. Smith and M. Soares-Santos and F. Sobreira and E. Suchyta and M. E. C. Swanson and G. Tarle and D. Thomas and B. E. Tucker and D. L. Tucker and V. Vikram and A. R. Walker and J. Weller and W. Wester and R. C. Wolf},
journal= {arXiv preprint arXiv:1709.00992},
year = {2018}
}
Comments
submitted to MNRAS