@article {233026,
	title = {Characterizing unknown systematics in large scale structure surveys},
	journal = {Journal of Cosmology and Astro-Particle Physics},
	volume = {04},
	year = {2014},
	month = {April 1, 2014},
	pages = {007},
	abstract = {Photometric large scale structure (LSS) surveys probe the largestvolumes in the Universe, but are inevitably limited by systematicuncertainties. Imperfect photometric calibration leads to biases in ourmeasurements of the density fields of LSS tracers such as galaxies andquasars, and as a result in cosmological parameter estimation. Earlierstudies have proposed using cross-correlations between differentredshift slices or cross-correlations between different surveys toreduce the effects of such systematics. In this paper we develop amethod to characterize unknown systematics. We demonstrate that while wedo not have sufficient information to correct for unknown systematics inthe data, we can obtain an estimate of their magnitude. We define aparameter to estimate contamination from unknown systematics usingcross-correlations between different redshift slices and proposediscarding bins in the angular power spectrum that lie outside a certaincontamination tolerance level. We show that this method improvesestimates of the bias using simulated data and further apply it tophotometric luminous red galaxies in the Sloan Digital Sky Survey as acase study.},
	isbn = {1475-7516},
	url = {http://adsabs.harvard.edu/abs/2014JCAP...04..007A},
	author = {Agarwal, Nishant and Ho, Shirley and Myers, Adam D. and Seo, Hee-Jong and Ross, Ashley J. and Bahcall, Neta and Brinkmann, Jonathan and Eisenstein, Daniel J. and Muna, Demitri and Palanque-Delabrouille, Nathalie and P{\^a}ris, Isabelle and Petitjean, Patrick and Schneider, Donald P. and Streblyanska, Alina and Weaver, Benjamin A. and Y{\`e}che, Christophe}
}
