{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.22050",
  "author_total": 1,
  "authors": [
    {
      "name": "Omer Imran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/brf21yp8jdyz5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.22050",
  "fulltext_url": null,
  "id": "brf21yp8jdyz5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.22050",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.pop-ph"
    ],
    [
      "openalex",
      "3103|Space Science and Extraterrestrial Life"
    ]
  ],
  "openalex": "W7222583325",
  "pdf_url": "https://arxiv.org/pdf/2610.22050",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/brf21yp8jdyz5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2610.22050",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.22050"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.pop-ph",
      "subfield": "general"
    }
  ],
  "title": "Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data",
  "title_text": "Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/brf21yp8jdyz5",
  "venue": "arXiv",
  "versions": []
}