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Radio frequency interference (RFI) mitigation remains a major challenge inthe search for radio technosignatures. Typical mitigation strategies include adirection-of-origin (DoO) filter, where a signal is classified as RFI if it isdetected in multiple directions on the sky. These classifications generallyrely on estimates of signal properties, such as frequency and frequency driftrate. Convolutional neural networks (CNNs) offer a promising complement toexisting filters because they can be trained to analyze dynamic spectradirectly, instead of relying on inferred signal properties. In this work, wecompiled several data sets consisting of labeled pairs of images of dynamicspectra, and we designed and trained a CNN that can determine whether or not asignal detected in one scan is also present in another scan. This CNN-based DoOfilter outperforms both a baseline 2D correlation model as well as existing DoOfilters over a range of metrics, with precision and recall values of 99.15% and97.81%, respectively. We found that the CNN reduces the number of signalsrequiring visual inspection after the application of traditional DoO filters bya factor of 6-16 in nominal situations.

A Machine-Learning-Based Direction-of-Origin Filter for the Identification of Radio Frequency Interference in the Search for Technosignatures