{
  "abstract": "Fast radio bursts (FRBs) are millisecond-duration extragalactic radio transients of great scientific value for studying the intergalactic medium and extreme astrophysical environments. Large-scale radio surveys such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST) produce petabytes of observational data annually, making efficient automated FRB identification an urgent priority. Traditional FRB search pipelines rely on computationally expensive dedispersion preprocessing, and existing deep learning methods struggle to extract weak dispersed burst features from heavy background and radio frequency interference (RFI) contamination. In this work, we address image-level FRB/non-FRB classification on dispersed dynamic spectra without dedispersion preprocessing. We propose FRBWaveletNet, a wavelet-enhanced convolutional framework that embeds explicit multiscale frequency decomposition into the network backbone. This design explicitly targets the multiscale morphological characteristics of dispersed FRBs, improving the representation of weak burst signals masked by interference. On a real FAST test set constructed from observations of FRB 20201124A, FRBWaveletNet attains 99.09% accuracy and an F1-score of 98.94%, outperforming matched convolutional neural network (CNN) baselines. Applied to four FAST repeating sources using sliding-window inference, it recovers 599 of 679 cataloged bursts. The results support the use of wavelet-based multiscale representation for FRB classification and characterize cataloged-burst recovery and false-positive candidate output in burst-associated FAST observations.",
  "abstract_shown": true,
  "affiliations": [
    "Southwest Petroleum University",
    "Qilu Normal University",
    "Shandong Key Laboratory of Space Environment and Exploration Technology",
    "State Key Laboratory of Radio Astronomy and Technology",
    "Swinburne University of Technology",
    "University of Nottingham Ningbo China",
    "Shanghai Dianji University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xinyu Gu",
      "orcid": "0009-0000-9426-7520"
    },
    {
      "affs": [
        0
      ],
      "name": "Wangxuan Zhu",
      "orcid": "0009-0003-4698-5108"
    },
    {
      "affs": [
        0
      ],
      "name": "Xianfeng Yang"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Zerui Wang",
      "orcid": "0000-0002-3883-6669"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Lei Zhang",
      "orcid": "0000-0001-8539-4237"
    },
    {
      "affs": [
        5
      ],
      "name": "Zheng Lu",
      "orcid": "0000-0003-4098-2486"
    },
    {
      "affs": [
        6
      ],
      "name": "Jie Zhang",
      "orcid": "0000-0003-0375-3553"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dedq9z9544cnv.bib",
  "cited_by": 0,
  "doi": "10.3847/1538-4357/aea21a",
  "fulltext_url": null,
  "id": "dedq9z9544cnv",
  "issns": [
    "0004-637X",
    "1538-4357"
  ],
  "landing_url": "https://iopscience.iop.org/article/10.3847/1538-4357/aea21a",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3103|Radio Astronomy Observations and Technology"
    ]
  ],
  "openalex": "W7215074303",
  "pdf_url": "https://iopscience.iop.org/article/10.3847/1538-4357/aea21a/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "American Astronomical Society",
  "ris": "https://research.draconitic.com/paper/dedq9z9544cnv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:11Z",
      "native_id": "10.3847/1538-4357/aea21a",
      "source": "crossref",
      "url": "https://iopscience.iop.org/article/10.3847/1538-4357/aea21a"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "journal-openalex:3103|Stellar, planetary, and galactic studies",
      "subfield": "stellar"
    }
  ],
  "title": "Fast Radio Burst Classification on Dispersed Dynamic Spectra Using a Wavelet-based Convolutional Network",
  "title_text": "Fast Radio Burst Classification on Dispersed Dynamic Spectra Using a Wavelet-based Convolutional Network",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:11Z",
  "url": "https://research.draconitic.com/paper/dedq9z9544cnv",
  "venue": "The Astrophysical Journal",
  "versions": []
}