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We develop a practical methodology to remove modes from a galaxy survey powerspectrum that are associated with systematic errors. We apply this to the BOSSCMASS sample, to see if it removes the excess power previously observed beyondthe best-fit $\Lambda$CDM model on very large scales. We consider severalpossible sources of data contamination, and check whether they affect thenumber of targets that can be observed and the power spectrum measurements. Wedescribe a general framework for how such knowledge can be transformed intotemplate fields. Mode subtraction can then be used to remove these systematiccontaminants at least as well as applying corrective weighting to the observedgalaxies, but benefits from giving an unbiased power. Even after applyingtemplates for all known systematics, we find a large-scale power excess, butthis is reduced compared with that observed using the weights provided by theBOSS team. This excess is at much larger scales than the BAO scale and does notaffect the main results of BOSS. However, it will be important for themeasurement of a scale-dependent bias due to primordial non-Gaussianity. Theexcess is beyond that allowed by any simple model of non-Gaussianity matchingPlanck data, and is not matched in other surveys. We show that this powerexcess can further be reduced but is still present using "phenomenological"templates, designed to consider further potentially unknown sources ofsystematic contamination. As all discrepant angular modes can be removed using"phenomenological" templates, the potentially remaining contaminant actsradially.

A map-based method for eliminating systematic modes from galaxy clustering power spectra with application to BOSS