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Electromagnetic (EM) observations of gravitational-wave (GW) sources wouldbring unique insights into a source which are not available from either channelalone. However EM follow-up of GW events presents new challenges. GW eventswill have large sky error regions, on the order of 10-100 square degrees, whichcan be made up of many disjoint patches. When searching such large areas thereis potential contamination by EM transients unrelated to the GW event.Furthermore, the characteristics of possible EM counterparts to GW events arealso uncertain. It is therefore desirable to be able to assess the statisticalsignificance of a candidate EM counterpart, which can only be done byperforming background studies of large data sets. Current image processingpipelines such as that used by ROTSE are not usually optimised for large-scaleprocessing. We have automated the ROTSE image analysis, and supplemented itwith a post-processing unit for candidate validation and classification. Wealso propose a simple ad hoc statistic for ranking candidates as more likely tobe associated with the GW trigger. We demonstrate the performance of theautomated pipeline and ranking statistic using archival ROTSE data. EMcandidates from a randomly selected set of images are compared to a backgroundestimated from the analysis of 102 additional sets of archival images. Thepipeline's detection efficiency is computed empirically by re-analysis of theimages after adding simulated optical transients that follow typical lightcurves for gamma-ray burst afterglows and kilonovae. We show that the automatedpipeline rejects most background events and is sensitive to simulatedtransients to limiting magnitudes consistent with the limiting magnitude of theimages.

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events