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Searches for counterparts to multimessenger events with optical imagers usedifference imaging to detect new transient sources. However, even with existingartifact detection algorithms, this process simultaneously returns severalclasses of false positives: false detections from poor quality imagesubtractions, false detections from low signal-to-noise images, and detectionsof pre-existing variable sources. Currently, human visual inspection to removethe false positives is a central part of multimessenger follow-up observations,but when next generation gravitational wave and neutrino detectors come onlineand increase the rate of multimessenger events, the visual inspection processwill be prohibitively expensive. We approach this problem with twoconvolutional neural networks operating on the difference imaging outputs. Thefirst network focuses on removing false detections and demonstrates an accuracyof 92 percent on our dataset. The second network focuses on sorting all realdetections by the probability of being a transient source within a host galaxyand distinguishes between various classes of images that previously requiredadditional human inspection. We find the number of images requiring humaninspection will decrease by a factor of 1.5 using our approach alone and afactor of 3.6 using our approach in combination with existing algorithms,facilitating rapid multimessenger counterpart identification by theastronomical community.

Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks