  The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will
discover an unprecedented number of supernovae (SNe), making spectroscopic
classification for all the events infeasible. LSST will thus rely on
photometric classification, whose accuracy depends on the not-yet-finalized
LSST observing strategy. In this work, we analyze the impact of cadence choices
on classification performance using simulated multi-band light curves. First,
we simulate SNe with an LSST baseline cadence, a non-rolling cadence, and a
presto-color cadence which observes each sky location three times per night
instead of twice. Each simulated dataset includes a spectroscopically-confirmed
training set, which we augment to be representative of the test set as part of
the classification pipeline. Then, we use the photometric transient
classification library snmachine to build classifiers. We find that the active
region of the rolling cadence used in the baseline observing strategy yields a
25% improvement in classification performance relative to the background
region. This improvement in performance in the actively-rolling region is also
associated with an increase of up to a factor of 2.7 in the number of
cosmologically-useful Type Ia supernovae relative to the background region.
However, adding a third visit per night as implemented in presto-color degrades
classification performance due to more irregularly sampled light curves.
Overall, our results establish desiderata on the observing cadence related to
classification of full SNe light curves, which in turn impacts photometric SNe
cosmology with LSST.
