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 follo...
| Authors | Omer Imran |
|---|---|
| Primary category | astro-ph.IM |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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