  Electromagnetic (EM) observations of gravitational-wave (GW) sources would
bring unique insights into a source which are not available from either channel
alone. However EM follow-up of GW events presents new challenges. GW events
will have large sky error regions, on the order of 10-100 square degrees, which
can be made up of many disjoint patches. When searching such large areas there
is potential contamination by EM transients unrelated to the GW event.
Furthermore, the characteristics of possible EM counterparts to GW events are
also uncertain. It is therefore desirable to be able to assess the statistical
significance of a candidate EM counterpart, which can only be done by
performing background studies of large data sets. Current image processing
pipelines such as that used by ROTSE are not usually optimised for large-scale
processing. We have automated the ROTSE image analysis, and supplemented it
with a post-processing unit for candidate validation and classification. We
also propose a simple ad hoc statistic for ranking candidates as more likely to
be associated with the GW trigger. We demonstrate the performance of the
automated pipeline and ranking statistic using archival ROTSE data. EM
candidates from a randomly selected set of images are compared to a background
estimated from the analysis of 102 additional sets of archival images. The
pipeline's detection efficiency is computed empirically by re-analysis of the
images after adding simulated optical transients that follow typical light
curves for gamma-ray burst afterglows and kilonovae. We show that the automated
pipeline rejects most background events and is sensitive to simulated
transients to limiting magnitudes consistent with the limiting magnitude of the
images.
