{
  "abstract": "Microwave Kinetic Inductance Detectors (MKIDs) are superconducting photon counting detectors that simultaneously measure the energy and arrival time of individual photons and are suitable for large-format arrays. The energy resolving power R = E/$Δ$E is the primary figure of merit for spectrophotometric applications. It is limited in practice by the assumptions underlying the Wiener optimal filter used to estimate photon energy: stationary noise, linear response, and energy independent pulse shapes, all of which MKIDs structurally violate. We present Venom (Very Efficient Neural Optimal-filter for MKIDs), a selective state space model based on the Mamba architecture that replaces the coordinate transform and optimal filter, operating directly on raw in-phase/quadrature timestream data, carrying 3,314 trainable parameters, and targeting deployment on the MKIDGen3 and future system-on-chip readout platforms. To deal with the small calibration datasets typical of MKID experiments, we train on synthetic pulses drawn from a streaming principal component analysis that samples photon energy continuously between calibration wavelengths. All numbers are reported on a stratified 15% held-out validation set, with R measured from a kernel density estimate. On an InHf bilayer resonator (ten wavelengths, 254-1310nm), Venom reaches a mean R of 26.4 versus 24.2 for the published per-wavelength optimal filter baseline, a 9% improvement from one model in place of ten per-laser filter templates. The R of 38.6 at 254 nm is the highest ever recorded for an ultraviolet-to-near-infrared MKID suitable for use in a dense array. On a PtSi resonator (five wavelengths, 808-1310nm), where the published analysis used one shared 920nm template, Venom matches the optimal filter, with a mean R of 9.1 versus 8.8 at the three interior wavelengths.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30382",
  "author_total": 1,
  "authors": [
    {
      "name": "Benjamin A. Mazin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8cz0rajp1k649.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30382",
  "fulltext_url": null,
  "id": "8cz0rajp1k649",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30382",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.30382",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8cz0rajp1k649.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2609.30382",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30382"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Selective state space model for photon energy estimation in microwave kinetic inductance detectors",
  "title_text": "Selective state space model for photon energy estimation in microwave kinetic inductance detectors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/8cz0rajp1k649",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/hz16-xh69",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}