Testing LSST Dither Strategies for Survey Uniformity and Large-Scale
  Structure Systematics
  The Large Synoptic Survey Telescope (LSST) will survey the southern sky from
2022--2032 with unprecedented detail. Since the observing strategy can lead to
artifacts in the data, we investigate the effects of telescope-pointing offsets
(called dithers) on the $r$-band coadded 5$\sigma$ depth yielded after the
10-year survey. We analyze this survey depth for several geometric patterns of
dithers (e.g., random, hexagonal lattice, spiral) with amplitude as large as
the radius of the LSST field-of-view, implemented on different timescales (per
season, per night, per visit). Our results illustrate that per night and per
visit dither assignments are more effective than per season. Also, we find that
some dither geometries (e.g., hexagonal lattice) are particularly sensitive to
the timescale on which the dithers are implemented, while others like random
dithers perform well on all timescales. We then model the propagation of depth
variations to artificial fluctuations in galaxy counts, which are a systematic
for large-scale structure studies. We calculate the bias in galaxy counts
caused by the observing strategy, accounting for photometric calibration
uncertainties, dust extinction, and magnitude cuts; uncertainties in this bias
limit our ability to account for structure induced by the observing strategy.
We find that after 10 years of the LSST survey, the best dither strategies lead
to uncertainties in this bias smaller than the minimum statistical floor for a
galaxy catalog as deep as $r$$<$27.5. A few of these strategies bring the
uncertainties close to the statistical floor for $r$$<$25.7 after only one year
of survey.
