  The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected
to process ${\sim}10^6$ transient detections per night. For precision
measurements of cosmological parameters and rates, it is critical to understand
the detection efficiency, magnitude limits, artifact contamination levels, and
biases in the selection and photometry. Here we rigorously test the LSST
Difference Image Analysis (DIA) pipeline using simulated images from the Rubin
Observatory LSST Dark Energy Science Collaboration (DESC) Data Challenge (DC2)
simulation for the Wide-Fast-Deep (WFD) survey area. DC2 is the first
large-scale (300 deg$^2$) image simulation of a transient survey that includes
realistic cadence, variable observing conditions, and CCD image artifacts. We
analyze ${\sim}$15 deg$^2$ of DC2 over a 5-year time-span in which artificial
point-sources from Type Ia Supernovae (SNIa) light curves have been overlaid
onto the images. We measure the detection efficiency as a function of
Signal-to-Noise Ratio (SNR) and find a $50\%$ efficiency at $\rm{SNR}=5.8$. The
magnitude limits for each filter are: $u=23.66$, $g=24.69$, $r=24.06$,
$i=23.45$, $z=22.54$, $y=21.62$ $\rm{mag}$. The artifact contamination is
$\sim90\%$ of detections, corresponding to $\sim1000$ artifacts/deg$^2$ in $g$
band, and falling to 300 per deg$^2$ in $y$ band. The photometry has biases
$<1\%$ for magnitudes $19.5 < m <23$. Our DIA performance on simulated images
is similar to that of the Dark Energy Survey pipeline applied to real images.
We also characterize DC2 image properties to produce catalog-level simulations
needed for distance bias corrections. We find good agreement between DC2 data
and simulations for distributions of SNR, redshift, and fitted light-curve
properties. Applying a realistic SNIa-cosmology analysis for redshifts $z<1$,
we recover the input cosmology parameters to within statistical uncertainties.
