Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data

Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before
a human looks at it. They are validated on held-out data from the training distribution, which excludes
the cases where they are most likely to fail: a technosignature has unknown morphology, and terrestrial
interference is unbounded in variety.
I trained a standard competition detector once, froze it, and probed it with 15,000 injections across ten
morphologies and five amplitudes, placed into real Green Bank backgrounds. Two directions were tested:
genuine signals with morphologies other than a straight narrowband line, and interference resembling the
training data.
Recall did not follow the predicted ordering, and it fell as signals brightened, even for a needle of
the kind the detector was trained on (from 0.590 to 0.090). One interference morphology reached a false
positive rate of 0.627, comparable to the detector’s own baseline recall of 0.5525 on genuine signals.
Gradient-weighted Class Activation Mapping (Grad-CAM) showed attention along the injected path
in one missed family by a pre-specified test, and visually in a second. A pre-specified, time-averaged
ON-OFF filter removed false alarms and genuine detections at the same rate.
The detector reads only the ON panels. Since the only evidence distinguishing a genuine candidate
from terrestrial interference lies in the discarded OFF panels, it cannot tell if a signal originates from
the target star; it can only tell if it resembles the training data. This explains the false alarm directly; the
missed signals reflect what the detector learned to recognise, which reading the OFF panels would not by
itself change.

Code, injection manifest, results, and frozen model weights: https://github.com/OmerImran/seti-two-directional-failure