{
  "abstract": "While radio measurements of extensive air showers have shown to achieve a high precision of $X_\\mathrm{max}$ sensitivity, it has been shown that parameters beyond $X_\\mathrm{max}$ can also be reconstructed. These shape parameters contain additional sensitivity to the hadronic physics in the shower as well as its mass composition. In this work, we showcase a reconstruction framework to recover the full longitudinal profile from realistic radio measurements. The framework is based on Information Field Theory that infers the full profile with a forward-based model, which uses a Gaisser-Hillas profile with weakly informative shower priors, SMIET with a template library to synthesise pulses at any event geometry, and a realistic antenna response and noise level emulating that of SKA-Low. We verify the self-consistency of our framework with $\\sim 900$ events generated with SMIET with antennas placed on the $\\vec{v} \\times (\\vec{v} \\times \\vec{B})$ axis. The framework recovers the full profile within uncertainty and capture correlations between shower parameters. We yield an $X_\\mathrm{max}$ resolution of $< 9$ g cm$^{-2}$ as well as resolutions of the width and asymmetry with minimal bias. The profile is also recovered with a bias of $< 4$% at all atmospheric depths $< 1200$ g cm$^{-2}$. We aim to apply this framework with pulses simulated from CoREAS with measured noise, ultimately extending the framework to realistic antenna layouts such as from LOFAR or SKA-Low.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: PoS(ARENA2026)037",
  "arxiv": "2608.30887",
  "author_total": 36,
  "authors": [
    {
      "name": "Keito Watanabe"
    },
    {
      "name": "Tim Huege"
    },
    {
      "name": "Torsten Enßlin"
    },
    {
      "name": "Vincent Eberle"
    },
    {
      "name": "Sjoerd Bouma"
    },
    {
      "name": "Justin Bray"
    },
    {
      "name": "Stijn Buitink"
    },
    {
      "name": "Arthur Corstanje"
    },
    {
      "name": "Vital De Henau"
    },
    {
      "name": "Edwin Dickinson"
    },
    {
      "name": "Tjibbe Gottmer"
    },
    {
      "name": "Brian Hare"
    },
    {
      "name": "Haoning He"
    },
    {
      "name": "Jörg Hörandel"
    },
    {
      "name": "Clancy James"
    },
    {
      "name": "Mrinal Jetti"
    },
    {
      "name": "Philipp Laub"
    },
    {
      "name": "Xingyu Li"
    },
    {
      "name": "Marten Lourens"
    },
    {
      "name": "Hermann-Josef Mathes"
    },
    {
      "name": "Katie Mulrey"
    },
    {
      "name": "Anna Nelles"
    },
    {
      "name": "Subhadip Saha"
    },
    {
      "name": "Felix Schlüter"
    },
    {
      "name": "Olaf Scholten"
    },
    {
      "name": "Ralph Spencer"
    },
    {
      "name": "Christopher Sterpka"
    },
    {
      "name": "Sander ter Veen"
    },
    {
      "name": "Karen Terveer"
    },
    {
      "name": "Gia Trinh"
    },
    {
      "name": "Paulina Turekova"
    },
    {
      "name": "Darko Veberič"
    },
    {
      "name": "Marc Waterson"
    },
    {
      "name": "Chao Zhang"
    },
    {
      "name": "Pengfei Zhang"
    },
    {
      "name": "Yi Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9z9fw0wfbg14d.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30887",
  "fulltext_url": null,
  "id": "9z9fw0wfbg14d",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30887",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2608.30887",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9z9fw0wfbg14d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "2608.30887",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30887"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Beyond $X_\\mathrm{max}$ : Reconstructing Air Shower Profiles with Information Field Theory with SKA-Low",
  "title_text": "Beyond X_max : Reconstructing Air Shower Profiles with Information Field Theory with SKA-Low",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/9z9fw0wfbg14d",
  "venue": "arXiv",
  "versions": []
}