Characterizing unknown systematics in large scale structure surveys
  Photometric large scale structure (LSS) surveys probe the largest volumes in
the Universe, but are inevitably limited by systematic uncertainties. Imperfect
photometric calibration leads to biases in our measurements of the density
fields of LSS tracers such as galaxies and quasars, and as a result in
cosmological parameter estimation. Earlier studies have proposed using
cross-correlations between different redshift slices or cross-correlations
between different surveys to reduce the effects of such systematics. In this
paper we develop a method to characterize unknown systematics. We demonstrate
that while we do not have sufficient information to correct for unknown
systematics in the data, we can obtain an estimate of their magnitude. We
define a parameter to estimate contamination from unknown systematics using
cross-correlations between different redshift slices and propose discarding
bins in the angular power spectrum that lie outside a certain contamination
tolerance level. We show that this method improves estimates of the bias using
simulated data and further apply it to photometric luminous red galaxies in the
Sloan Digital Sky Survey as a case study.
