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We implement a linear model for mitigating the effect of observing conditionsand other sources of contamination in galaxy clustering analyses. Our treatmentimproves upon the fiducial systematics treatment of the Dark Energy Survey(DES) Year 1 (Y1) cosmology analysis in four crucial ways. Specifically, ourtreatment: 1) does not require decisions as to which observable systematics aresignificant and which are not, allowing for the possibility of multiple mapsadding coherently to give rise to significant bias even if no single map leadsto a significant bias by itself; 2) characterizes both the statistical andsystematic uncertainty in our mitigation procedure, allowing us to propagatesaid uncertainties into the reported cosmological constraints; 3) explicitlyexploits the full spatial structure of the galaxy density field todifferentiate between cosmology-sourced and systematics-sourced fluctuationswithin the galaxy density field; 4) is fully automated, and can therefore betrivially applied to any data set. The updated correlation function for the DESY1 redMaGiC catalog minimally impacts the cosmological posteriors from thatanalysis. Encouragingly, our analysis does improve the goodness of fitstatistic of the DES Y1 3$\times$2pt data set ($\Delta \chi^2 = -6.5$ with noadditional parameters). This improvement is due in nearly equal parts to boththe change in the correlation function and the added statistical and systematicuncertainties associated with our method. We expect the difference inmitigation techniques to become more important in future work as the size ofcosmological data sets grows.

Linear Systematics Mitigation in Galaxy Clustering in the Dark Energy Survey Year 1 Data