{
  "abstract": "The cosmic distance duality relation (CDDR), $D_L(z)(1+z)^{-2}/D_A(z) \\equiv 1$, is a fundamental relation in modern cosmology linking luminosity distance $D_L$ and angular diameter distance $D_A$. We define $η(z) \\equiv D_L(z)(1+z)^{-2}/D_A(z)$ and reconstruct the relevant quantities over $0 < z < 2.5$ using two independent non-parametric methods: Gaussian processes (GP) and artificial neural networks (ANN). Specifically, we reconstruct $D_L$ from three different Type Ia supernovae (SNe$~$Ia) compilations (PantheonPlus, Union3, and DES-Dovekie), the baryon acoustic oscillation distance ratio $D_M/D_H$ from SDSS and DESI, and the Hubble function $H(z)$ from cosmic chronometer (CC) data. Our CDDR test is independent of both the cosmological model and the sound horizon $r_d$ calibration, as $D_M/D_H$ naturally eliminates $r_d$. We render our GP reconstructions insensitive to mean-function and kernel choices via full Bayesian marginalization over hyperparameters, with ANN as a cross-check$-$revealing the impact of data sparsity on the latter. Our results show no significant deviation from the CDDR exceeding $2σ$, and for the CC+BAO+DES-Dovekie combination it remains within $1σ$ over most of the redshift range. With $r_d$ eliminated, varying the only remaining external parameter $M_B$ systematically shifts $η(z)$ through the $M_B$-$H_0$ degeneracy, indicating that the observed $η\\neq1$ signal is closely tied to $H_0$ tension among datasets and is likely driven by $M_B$ systematics rather than a true CDDR violation. Future high-quality CC, BAO, and SNe$~$Ia data$-$with either improved $M_B$ calibration or $M_B$-free distance measurements$-$will be essential for extending this calibration-free approach.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.24203",
  "author_total": 3,
  "authors": [
    {
      "name": "Bo-Hao Jiang"
    },
    {
      "name": "Guo-Jian Wang"
    },
    {
      "name": "Tao Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4vdg6wtxa32e9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24203",
  "fulltext_url": null,
  "id": "4vdg6wtxa32e9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24203",
  "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.IM"
    ],
    [
      "openalex",
      "3103|Cosmology and Gravitation Theories"
    ]
  ],
  "openalex": "W7214039548",
  "pdf_url": "https://arxiv.org/pdf/2609.24203",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4vdg6wtxa32e9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.24203",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24203"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Sound-Horizon-Independent Test of Cosmic Distance Duality Relation Using Artificial Neural Networks and Gaussian Processes",
  "title_text": "Sound-Horizon-Independent Test of Cosmic Distance Duality Relation Using Artificial Neural Networks and Gaussian Processes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/4vdg6wtxa32e9",
  "venue": "arXiv",
  "versions": []
}