Linear Systematics Mitigation in Galaxy Clustering in the Dark Energy
  Survey Year 1 Data
  We implement a linear model for mitigating the effect of observing conditions
and other sources of contamination in galaxy clustering analyses. Our treatment
improves upon the fiducial systematics treatment of the Dark Energy Survey
(DES) Year 1 (Y1) cosmology analysis in four crucial ways. Specifically, our
treatment: 1) does not require decisions as to which observable systematics are
significant and which are not, allowing for the possibility of multiple maps
adding coherently to give rise to significant bias even if no single map leads
to a significant bias by itself; 2) characterizes both the statistical and
systematic uncertainty in our mitigation procedure, allowing us to propagate
said uncertainties into the reported cosmological constraints; 3) explicitly
exploits the full spatial structure of the galaxy density field to
differentiate between cosmology-sourced and systematics-sourced fluctuations
within the galaxy density field; 4) is fully automated, and can therefore be
trivially applied to any data set. The updated correlation function for the DES
Y1 redMaGiC catalog minimally impacts the cosmological posteriors from that
analysis. Encouragingly, our analysis does improve the goodness of fit
statistic of the DES Y1 3$\times$2pt data set ($\Delta \chi^2 = -6.5$ with no
additional parameters). This improvement is due in nearly equal parts to both
the change in the correlation function and the added statistical and systematic
uncertainties associated with our method. We expect the difference in
mitigation techniques to become more important in future work as the size of
cosmological data sets grows.
