  As it stands today, the search for extraterrestrial intelligence (SETI) is
highly dependent on our ability to detect interesting candidate signals, or
technosignatures, in radio telescope observations and distinguish these from
human radio frequency interference (RFI). Current signal search pipelines look
for signals in spectrograms of intensity as a function of time and frequency
(which can be thought of as images), but tend to do poorly in identifying
multiple signals in a single data frame. This is especially apparent when there
are dim signals in the same frame as bright, high signal-to-noise ratio (SNR)
signals. In this work, we approach this problem using convolutional neural
networks (CNN) as a computationally efficient method for localizing signals in
synthetic observations resembling data collected by Breakthrough Listen using
the Green Bank Telescope. We generate two synthetic datasets, the first with
exactly one signal at various SNR levels and the second with exactly two
signals, one of which represents RFI. We find that a residual CNN with strided
convolutions and using multiple image normalizations as input outperforms a
more basic CNN with max pooling trained on inputs with only one normalization.
Training each model on a smaller subset of the training data at higher SNR
levels results in a significant increase in model performance, reducing root
mean square errors by at least a factor of 3 at an SNR of 25 dB. Although each
model produces outliers with significant error, these results demonstrate that
using CNNs to analyze signal location is promising, especially in image frames
that are crowded with multiple signals.
