  The Large Synoptic Survey Telescope (LSST) will photometrically monitor
approximately 1 billion stars for ten years. The resulting light curves can be
used to detect transiting exoplanets. In particular, as demonstrated by Lund et
al. (2015), LSST will probe stellar populations currently undersampled in most
exoplanet transit surveys, including out to extragalactic distances. In this
paper we test the efficiency of the box-fitting least-squares (BLS) algorithm
for accurately recovering the periods of transiting exoplanets using simulated
LSST data. We model planets with a range of radii orbiting a solar-mass star at
a distance of 7 kpc, with orbital periods ranging from 0.5 to 20 days. We find
that standard-cadence LSST observations will be able to reliably recover the
periods of Hot Jupiters with periods shorter than approximately 3 days, however
it will remain a challenge to confidently distinguish these transiting planets
from false positives. At the same time, we find that the LSST deep drilling
cadence is extremely powerful: the BLS algorithm successfully recovers at least
30% of sub-Saturn-size exoplanets with orbital periods as long as 20 days, and
a simple BLS power criterion robustly distinguishes approximately 98% of these
from photometric (i.e. statistical) false positives.
