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    "story_id": "mp-2026-10-28-018",
    "source_story_id": "tmp-brief-fpga-calorimeter-trigger",
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    "headline": "A Full Calorimeter Image Reaches the Trigger in 25.9 Microseconds",
    "slug": "a-full-calorimeter-image-reaches-the-trigger-in-25-9-microseconds",
    "dek": "An 8-bit FPGA CNN rejected 99.34 percent of a recorded background sample at nominal 95 percent signal efficiency within the adopted latency budget.",
    "summary": "An 8-bit FPGA CNN rejected 99.34 percent of a recorded background sample at nominal 95 percent signal efficiency within the adopted latency budget.",
    "body_text": "A lossless space-to-depth transform and quantization-aware training shrink full-granularity calorimeter inference for FPGA execution. On 100,000 test events, the board measured 17.9 microseconds for CNN inference and a projected 25.9 microseconds end to end, below the study's 32-microsecond budget. The result is a KOTO-oriented proof of principle, not a deployed trigger claim.",
    "why_it_matters": "An 8-bit FPGA CNN rejected 99.34 percent of a recorded background sample at nominal 95 percent signal efficiency within the adopted latency budget.",
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      "The result is a KOTO-oriented proof of principle, not a deployed trigger claim."
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    "first_published_at": "2026-10-28T09:00:00.000-04:00",
    "modified_at": "2026-10-28T09:00:00.000-04:00",
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    "tags": [
      "particle physics",
      "FPGA",
      "CNN",
      "real-time systems"
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  "sources": [
    {
      "source_id": "source-2026-10-28-020",
      "title": "arXiv preprint 2610.36720, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.36720v1",
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  "cite_this_report": {
    "title": "A Full Calorimeter Image Reaches the Trigger in 25.9 Microseconds",
    "publisher": "The Machine Press",
    "published_at": "2026-10-28T09:00:00.000-04:00",
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