{
  "abstract": "We present the first application of neural-network-based baryon acoustic oscillation (BAO) reconstruction to real galaxy survey data, restoring the acoustic signature damped by nonlinear structure growth. {\\texttt{NERV}} ({\\bf N}eural-network {\\bf E}nhanced {\\bf R}econstruction of the Uni{\\bf V}erse) augments standard reconstruction with a convolutional neural network trained on cubic $N$-body simulations, and explicitly accounts for realistic observational effects including the curved-sky geometry, the redshift-dependent selection function, and finite survey boundaries, by tessellating the survey volume into local patches. We validate the method on the \\textsc{MultiDark-Patchy} mock catalogs, recovering unbiased BAO dilation parameters. Applied to the BOSS DR12 galaxy sample, NERV improves the precision of the BAO distance measurements significantly. These results establish neural reconstruction as a practical component of BAO analyses for ongoing surveys such as DESI, with the potential to substantially tighten constraints on the cosmic expansion history and the nature of dark energy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.15943",
  "author_total": 4,
  "authors": [
    {
      "name": "Shi-Hui Zang"
    },
    {
      "name": "Hong-Ming Zhu"
    },
    {
      "name": "Tian-Xiang Mao"
    },
    {
      "name": "Ue-Li Pen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5p1fc1t300nar.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.15943",
  "fulltext_url": null,
  "id": "5p1fc1t300nar",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.15943",
  "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"
    ],
    [
      "openalex",
      "3103|Cosmology and Gravitation Theories"
    ]
  ],
  "openalex": "W7213293090",
  "pdf_url": "https://arxiv.org/pdf/2609.15943",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5p1fc1t300nar.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.15943",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.15943"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    }
  ],
  "title": "NERV: Neural-network Enhanced Reconstruction of the UniVerse with Application to Baryon Acoustic Oscillations in the BOSS DR12 Galaxy Sample",
  "title_text": "NERV: Neural-network Enhanced Reconstruction of the UniVerse with Application to Baryon Acoustic Oscillations in the BOSS DR12 Galaxy Sample",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/5p1fc1t300nar",
  "venue": "arXiv",
  "versions": []
}