{
  "abstract": "The Neutrino Observatory in the Nanhai (NEON) is a proposed deep-sea neutrino telescope deployed in the South China Sea. Accurate reconstruction of shower-like events is crucial for neutrino energy measurements and multi-messenger astronomy, yet it poses significant challenges due to seawater optical attenuation, irregular detector geometry, and substantial $^{40}\\mathrm{K}$ ambient background. In this work, we present the first comprehensive reconstruction framework for shower-like events in NEON, encompassing both a physics-driven maximum likelihood estimation (MLE) method and a data-driven Graph Neural Network (GNN). The traditional MLE framework integrates spatial-isochronic hit selection, vertex reconstruction via time-residual M-estimator minimization, and decoupled directional and energy estimation based on pre-computed photon distribution tables. Physical calibrations, including PMT angular acceptance, hit-level time slewing corrections, and an effective line-source shower extension, are incorporated into the likelihood formulation. In parallel, a two-stage GNN is developed to capture intra-DOM PMT correlations and distance-weighted inter-DOM topological patterns. Simulation studies show that the MLE method achieves an overall median angular resolution of $4.19^\\circ$ and an energy resolution of 25\\%-37\\% over 1 TeV to 1 PeV with negligible systematic bias. The GNN further improves reconstruction fidelity in the low-to-intermediate energy regime, achieving a median angular resolution of $1.8^\\circ$ at 30 TeV and an energy resolution of $\\sim$ 20\\% between 40 and 300 TeV. Based on these reconstruction performances, the effective area and point-source discovery potential of NEON are evaluated. This framework establishes an essential reconstruction benchmark for NEON and provides practical methodologies for future next-generation deep-sea neutrino telescopes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03417",
  "author_total": 6,
  "authors": [
    {
      "name": "Sujie Lin"
    },
    {
      "name": "Weiqin Huang"
    },
    {
      "name": "Yihan Liu"
    },
    {
      "name": "Chengyu Shao"
    },
    {
      "name": "Lili Yang"
    },
    {
      "name": "Huiming Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9pt100604km7e.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03417",
  "fulltext_url": null,
  "id": "9pt100604km7e",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.03417",
  "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"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "openalex",
      "3106|Neutrino Physics Research"
    ]
  ],
  "openalex": "W7208751975",
  "pdf_url": "https://arxiv.org/pdf/2609.03417",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9pt100604km7e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:31Z",
      "native_id": "2609.03417",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03417"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Reconstruction of Shower-like Events in NEON Using Likelihood and Graph Neural Network Methods",
  "title_text": "Reconstruction of Shower-like Events in NEON Using Likelihood and Graph Neural Network Methods",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:31Z",
  "url": "https://research.draconitic.com/paper/9pt100604km7e",
  "venue": "arXiv",
  "versions": []
}