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arXiv Machine Learning By Omer Imran

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

Read the original on arXiv Machine Learning →

The study evaluates a convolutional neural network (CNN) technosignature detector trained on Breakthrough Listen data by injecting 15,000 synthetic signals of varied morphologies and amplitudes into real Green Bank Observatory backgrounds. Results show that the detector’s recall decreases with increasing signal brightness and fails to detect certain signal morphologies, while some interference patterns produce false positive rates comparable to its baseline recall. The analysis reveals that the detector only processes ON panels, lacking the OFF panel information needed to distinguish genuine extraterrestrial signals from terrestrial interference, which explains both the false alarms and missed detections.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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