{
  "abstract": "Temporal gaps in Swift-XRT X-ray afterglows of gamma-ray bursts (GRBs) limit how precisely the standard empirical plateau function constrains three parameters: the plateau end time $T_a$, the plateau flux $F_a$, and the post-plateau decay index $α$. These parameters underpin the luminosity-time and fundamental-plane correlations of GRB plateaus so that smaller measurement uncertainties can improve the statistical power of GRBs as cosmological probes. As the fifth study in a series on light-curve reconstruction, this work benchmarks four models on a parent sample of 545 Swift-XRT GRBs: a Physics-Informed Neural Network (PINN) with seven afterglow-motivated priors, ReFANN, a Siamese dual-branch network with Monte Carlo dropout, and Polynomial Quantile Regression (PQR). All four reduce the fractional uncertainties in $\\log T_a$, $\\log F_a$, and $α$ relative to the original observations. The PINN with a broken power-law prior gives the largest reductions ($\\sim$41-49\\%), at a higher outlier rate ($\\sim$15-20\\%), while ReFANN, Siamese network and PQR give more modest reductions ($\\sim$19-27\\%) with outlier fractions $\\lesssim$4\\%, across a wider range of light-curve morphologies. A reduced-$χ^2$ prior-selection scheme recovers a morphological classification consistent with independent labelling, though it does not match the best single-prior reduction. Together, these results provide a systematic benchmark of physics-informed and data-driven reconstruction strategies for irregularly sampled GRB afterglows.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.15261",
  "author_total": 6,
  "authors": [
    {
      "name": "Ayush Garg"
    },
    {
      "name": "Ritik Kumar"
    },
    {
      "name": "Maria G. Dainotti"
    },
    {
      "name": "Vipul Sharma"
    },
    {
      "name": "Amit Shukla"
    },
    {
      "name": "Dieter H. Hartmann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4tj0p16z4jtkq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.15261",
  "fulltext_url": null,
  "id": "4tj0p16z4jtkq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.15261",
  "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.CO"
    ],
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "openalex",
      "3103|Gamma-ray bursts and supernovae"
    ]
  ],
  "openalex": "W7213271548",
  "pdf_url": "https://arxiv.org/pdf/2609.15261",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4tj0p16z4jtkq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2609.15261",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.15261"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    }
  ],
  "title": "Physics-Informed Neural Networks and Data-Driven Models for GRB X-ray Light-Curve Gap Reconstruction",
  "title_text": "Physics-Informed Neural Networks and Data-Driven Models for GRB X-ray Light-Curve Gap Reconstruction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/4tj0p16z4jtkq",
  "venue": "arXiv",
  "versions": []
}