{
  "abstract": "Reconstructing the initial conditions of the Universe from late-time tracers would unlock cosmological information buried by non-linear structure formation and astrophysics. We reconstruct the initial density field corresponding to $z=300$, with its linear amplitude extrapolated to $z=0$, from simulated 21-cm and CO(1-0) line-intensity maps at $z=8$ generated with LIMFAST. Using a three-dimensional U-Net, we reconstruct the initial conditions and evaluate their impact on cosmological parameter constraints. The two tracers probe complementary environments: 21-cm emission traces neutral, low-density regions of the IGM, while CO traces overdense, star-forming regions. We also train and evaluate the reconstruction on maps incorporating SKA1-Low- and COMAP-ERA-like instrumental response and thermal noise. In the noiseless case, combining both tracers delivers the most accurate reconstruction across ionisation states, with the cross-correlation coefficient between the reconstructed and true initial density fields, $|C(k)| \\gtrsim 0.90$ for $k \\lesssim 0.75\\,{\\rm Mpc}^{-1}$. With observational effects, small-scale information is degraded, but the joint reconstruction retains substantial large-scale coherence, with $|C(k)| \\gtrsim 0.70$ for $k \\lesssim 0.3\\,{\\rm Mpc}^{-1}$. We quantify cosmological information gains through simulation-based inference using power-spectrum summaries pre- and post-reconstruction. The largest improvements occur for $σ_8$ and $n_{\\rm s}$, with a median aggregate marginal compression of $1.17$ bits in the noiseless case, corresponding to a $56\\%$ reduction in the factorised product of prior-normalised $68\\%$ marginal widths. With observational effects, the corresponding median compression is $1.29$ bits. These results demonstrate that joint 21-cm-CO reconstruction recovers cosmological information beyond pre-reconstruction summaries.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05412",
  "author_total": 5,
  "authors": [
    {
      "name": "Anchal Saxena"
    },
    {
      "name": "P. Daniel Meerburg"
    },
    {
      "name": "Guochao Sun"
    },
    {
      "name": "Tzu-Ching Chang"
    },
    {
      "name": "Lluís Mas-Ribas"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ejv6vy5hx5szr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05412",
  "fulltext_url": null,
  "id": "ejv6vy5hx5szr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05412",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2609.05412",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ejv6vy5hx5szr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:15Z",
      "native_id": "2609.05412",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05412"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Tracing the Cosmic Origins: Machine Learning Reconstruction of the Primordial Density Field from EoR Observations",
  "title_text": "Tracing the Cosmic Origins: Machine Learning Reconstruction of the Primordial Density Field from EoR Observations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:15Z",
  "url": "https://research.draconitic.com/paper/ejv6vy5hx5szr",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1093/mnras/stag1972",
      "pid": "7j6p8ma015tvc",
      "relation": "published-as",
      "title": "Tracing the Cosmic Origins: Machine Learning Reconstruction of the Primordial Density Field from EoR Observations",
      "venue": "Monthly Notices of the Royal Astronomical Society"
    }
  ]
}