Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

  title={Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks},
  author={A. Shandonay and Robert Morgan and Keith C. Bechtol and Clecio Roque De Bom and Brian Nord and A. Garcia and Ben Henghes and Ken Herner and M. Tabbutt and Antonella Palmese and Luidhy Santana-Silva and Marcelle Soares-Santos and M. S. S. Gill and Juan Garc{\'i}a-Bellido},
  journal={The Astrophysical Journal},
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 preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of… 


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