We develop a practical methodology to remove modes from a galaxy survey power spectrum that are associated with systematic errors. We apply this to the BOSS CMASS sample, to see if it removes the excess power previously observed beyond the best-fit $\Lambda$CDM model on very large scales. We consider several possible sources of data contamination, and check whether they affect the number of targets that can be observed and the power spectrum measurements. We describe a general framework for how such knowledge can be transformed into template fields. Mode subtraction can then be used to remove these systematic contaminants at least as well as applying corrective weighting to the observed galaxies, but benefits from giving an unbiased power. Even after applying templates for all known systematics, we find a large-scale power excess, but this is reduced compared with that observed using the weights provided by the BOSS team. This excess is at much larger scales than the BAO scale and does not affect the main results of BOSS. However, it will be important for the measurement of a scale-dependent bias due to primordial non-Gaussianity. The excess is beyond that allowed by any simple model of non-Gaussianity matching Planck data, and is not matched in other surveys. We show that this power excess can further be reduced but is still present using "phenomenological" templates, designed to consider further potentially unknown sources of systematic contamination. As all discrepant angular modes can be removed using "phenomenological" templates, the potentially remaining contaminant acts radially.
|Journal||Monthly Notices of the Royal Astronomical Society|
|Early online date||1 Oct 2018|
|Publication status||Early online - 1 Oct 2018|
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Data availability statement for 'A map-based method for eliminating systematic modes from galaxy clustering power spectra with application to BOSS'.
Kalus, B. (Creator), Percival, W. (Creator), Bacon, D. (Creator), Mueller, E. (Creator), Samushia, L. (Creator), Verde, L. (Creator), Ross, A. J. (Creator) & Bernal, J. L. (Creator), Oxford University Press, 1 Oct 2018
Dataset: Data Availability Statement