{
  "abstract": "Cadmium zinc telluride (CZT) detectors offer versatility, operational simplicity, and room-temperature X- and gamma-ray spectroscopy, making them attractive for collider applications, yet their use under high-flux conditions remains limited. Here, we present a preliminary feature-based pulse-shape analysis employing machine learning, on data acquired with a quasi-hemispherical CZT detector at the DA$\\Φ$NE beam test facility of the National Laboratory of Frascati of INFN. A 300-MeV electron beam impinging on a lead target produced characteristic Pb X-rays together with a broad background extending up to the electron-positron annihilation region. Physically motivated temporal and morphological features were extracted from the recorded waveforms and used to distinguish nominal photon-like pulses from anomalous events. An XGBoost classifier trained and validated on 10,000 labeled waveforms achieved an accuracy of approximately 97%, with most of its classification performance reached using only a few hundred labeled examples. The trained model was applied to more than 700,000 events, substantially reducing the spectral continuum and coincidence peaks, while preserving the characteristic Pb X-ray lines up to the 511-keV annihilation peak. These preliminary results demonstrate the potential of machine-learning-assisted pulse-shape discrimination for improving CZT spectroscopy in collider environments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.25156",
  "author_total": 39,
  "authors": [
    {
      "name": "Simone Manti"
    },
    {
      "name": "Francesco Artibani"
    },
    {
      "name": "Leonardo Abbene"
    },
    {
      "name": "Massimiliano Bazzi"
    },
    {
      "name": "Manuele Bettelli"
    },
    {
      "name": "Giacomo Borghi"
    },
    {
      "name": "Damir Bosnar"
    },
    {
      "name": "Mario Bragadireanu"
    },
    {
      "name": "Antonino Buttacavoli"
    },
    {
      "name": "Mario Carminati"
    },
    {
      "name": "Alberto Clozza"
    },
    {
      "name": "Francesco Clozza"
    },
    {
      "name": "Luca De Paolis"
    },
    {
      "name": "Raffaele Del Grande"
    },
    {
      "name": "Kamil Dulski"
    },
    {
      "name": "Carlo Fiorini"
    },
    {
      "name": "Ivica Friščić"
    },
    {
      "name": "Gaetano Gerardi"
    },
    {
      "name": "Carlo Guaraldo"
    },
    {
      "name": "Mihai Iliescu"
    },
    {
      "name": "Masa Iwasaki"
    },
    {
      "name": "Alexander Khreptak"
    },
    {
      "name": "Johan Marton"
    },
    {
      "name": "Pawel Moskal"
    },
    {
      "name": "Hiroaki Ohnishi"
    },
    {
      "name": "Kristian Piscicchia"
    },
    {
      "name": "Fabio Principato"
    },
    {
      "name": "Alessandro Scordo"
    },
    {
      "name": "Francesco Sgaramella"
    },
    {
      "name": "Michał Silarski"
    },
    {
      "name": "Diana Sirghi"
    },
    {
      "name": "Florin Sirghi"
    },
    {
      "name": "Magdalena Skurzok"
    },
    {
      "name": "Antonio Spallone"
    },
    {
      "name": "Kairo Toho"
    },
    {
      "name": "Oton Vazquez Doce"
    },
    {
      "name": "Andrea Zappettini"
    },
    {
      "name": "Johann Zmeskal"
    },
    {
      "name": "Catalina Curceanu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ftvctm20p6f7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.25156",
  "fulltext_url": null,
  "id": "7ftvctm20p6f7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.25156",
  "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",
      "nucl-ex"
    ],
    [
      "arxiv",
      "physics.ins-det"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.25156",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7ftvctm20p6f7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2609.25156",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.25156"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:nucl-ex",
      "subfield": "nuclear"
    }
  ],
  "title": "Pulse-shape discrimination with machine learning for CZT detectors at the DA$Φ$NE beam test facility",
  "title_text": "Pulse-shape discrimination with machine learning for CZT detectors at the DAΦNE beam test facility",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/7ftvctm20p6f7",
  "venue": "arXiv",
  "versions": []
}