Searches for counterparts to multimessenger events with optical imagers use
difference imaging to detect new transient sources. However, even with existing
artifact detection algorithms, this process simultaneously returns several
classes of false positives: false detections from poor quality image
subtractions, false detections from low signal-to-noise images, and detections
of pre-existing variable sources. Currently, human visual inspection to remove
the false positives is a central part of multimessenger follow-up observations,
but when next generation gravitational wave and neutrino detectors come online
and increase the rate of multimessenger events, the visual inspection process
will be prohibitively expensive. We approach this problem with two
convolutional neural networks operating on the difference imaging outputs. The
first network focuses on removing false detections and demonstrates an accuracy
of 92 percent on our dataset. The second network focuses on sorting all real
detections by the probability of being a transient source within a host galaxy
and distinguishes between various classes of images that previously required
additional human inspection. We find the number of images requiring human
inspection will decrease by a factor of 1.5 using our approach alone and a
factor of 3.6 using our approach in combination with existing algorithms,
facilitating rapid multimessenger counterpart identification by the
astronomical community.
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