English

Cross-Correlation Redshift Calibration Without Spectroscopic Calibration Samples in DES Science Verification Data

Cosmology and Nongalactic Astrophysics 2018-04-11 v1

Abstract

Galaxy cross-correlations with high-fidelity redshift samples hold the potential to precisely calibrate systematic photometric redshift uncertainties arising from the unavailability of complete and representative training and validation samples of galaxies. However, application of this technique in the Dark Energy Survey (DES) is hampered by the relatively low number density, small area, and modest redshift overlap between photometric and spectroscopic samples. We propose instead using photometric catalogs with reliable photometric redshifts for photo-z calibration via cross-correlations. We verify the viability of our proposal using redMaPPer clusters from the Sloan Digital Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS spectroscopic galaxies. We demonstrate how to combine photo-z with cross-correlation data to calibrate photometric redshift biases while marginalizing over possible clustering bias evolution in either the calibration or unknown photometric samples. We apply our method to DES Science Verification (DES SV) data in order to constrain the photometric redshift distribution of a galaxy sample selected for weak lensing studies, constraining the mean of the tomographic redshift distributions to a statistical uncertainty of Δz±0.01\Delta z \sim \pm 0.01. We forecast that our proposal can in principle control photometric redshift uncertainties in DES weak lensing experiments at a level near the intrinsic statistical noise of the experiment over the range of redshifts where redMaPPer clusters are available. Our results provide strong motivation to launch a program to fully characterize the systematic errors from bias evolution and photo-z shapes in our calibration procedure.

Keywords

Cite

@article{arxiv.1707.08256,
  title  = {Cross-Correlation Redshift Calibration Without Spectroscopic Calibration Samples in DES Science Verification Data},
  author = {C. Davis and E. Rozo and A. Roodman and A. Alarcon and R. Cawthon and M. Gatti and H. Lin and R. Miquel and E. S. Rykoff and M. A. Troxel and P. Vielzeuf and T. M. C. Abbott and F. B. Abdalla and S. Allam and J. Annis and K. Bechtol and A. Benoit-Lévy and E. Bertin and D. Brooks and E. Buckley-Geer and D. L. Burke and A. Carnero Rosell and M. Carrasco Kind and J. Carretero and F. J. Castander and M. Crocce and C. E. Cunha and C. B. D'Andrea and L. N. da Costa and S. Desai and H. T. Diehl and P. Doel and A. Drlica-Wagner and A. Fausti Neto and B. Flaugher and P. Fosalba and J. Frieman and J. García-Bellido and E. Gaztanaga and D. W. Gerdes and T. Giannantonio and D. Gruen and R. A. Gruendl and G. Gutierrez and K. Honscheid and B. Jain and D. J. James and T. Jeltema and E. Krause and K. Kuehn and S. Kuhlmann and N. Kuropatkin and O. Lahav and T. S. Li and M. Lima and M. March and J. L. Marshall and P. Martini and P. Melchior and R. L. C. Ogando and A. A. Plazas and A. K. Romer and E. Sanchez and V. Scarpine and R. Schindler and M. Schubnell and I. Sevilla-Noarbe and M. Smith and M. Soares-Santos and F. Sobreira and E. Suchyta and M. E. C. Swanson and G. Tarle and D. Thomas and V. Vikram and A. R. Walker and R. H. Wechsler},
  journal= {arXiv preprint arXiv:1707.08256},
  year   = {2018}
}

Comments

12 pages, 5 figures. Submitted to MNRAS. Comments welcome!

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