  We present a method that enables wide field ground-based telescopes to scan
the sky for sub-second stellar variability. The method has operational and
image processing components. The operational component is to take star trail
images. Each trail serves as a light curve for its corresponding source and
facilitates sub-exposure photometry. We train a deep neural network to identify
stellar variability in wide-field star trail images. We use the Large Synoptic
Survey Telescope (LSST) Photon Simulator to generate simulated star trail
images and include transient bursts as a proxy for variability. The network
identifies transient bursts on timescales down to 10 milliseconds. We argue
that there are multiple fields of astrophysics that can be advanced by the
unique combination of time resolution and observing throughput that our method
offers.
