  Radio frequency interference (RFI) mitigation remains a major challenge in
the search for radio technosignatures. Typical mitigation strategies include a
direction-of-origin (DoO) filter, where a signal is classified as RFI if it is
detected in multiple directions on the sky. These classifications generally
rely on estimates of signal properties, such as frequency and frequency drift
rate. Convolutional neural networks (CNNs) offer a promising complement to
existing filters because they can be trained to analyze dynamic spectra
directly, instead of relying on inferred signal properties. In this work, we
compiled several data sets consisting of labeled pairs of images of dynamic
spectra, and we designed and trained a CNN that can determine whether or not a
signal detected in one scan is also present in another scan. This CNN-based DoO
filter outperforms both a baseline 2D correlation model as well as existing DoO
filters over a range of metrics, with precision and recall values of 99.15% and
97.81%, respectively. We found that the CNN reduces the number of signals
requiring visual inspection after the application of traditional DoO filters by
a factor of 6-16 in nominal situations.
