Tracing the Cosmic Origins: Machine Learning Reconstruction of the Primordial Density Field from EoR Observations

Clicks: 12
ID: 332527
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,097 of 1,327 articles by views in monthly notices of the royal astronomical society

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,327 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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 the semi-numerical code, 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 intergalactic medium, while CO traces overdense, star-forming regions. We additionally 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)| ≳ 0.90 for k ≲ 0.75 Mpc−1. With observational effects, small-scale information is degraded, but the joint reconstruction retains substantial large-scale coherence, with |C(k)| ≳ 0.70 for k ≲ 0.3 Mpc−1. To quantify the cosmological information gain, we perform simulation-based inference using power-spectrum summaries pre- and post-reconstruction. Across an ensemble of realisations, reconstruction yields the largest improvements for σ8 and ns, 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 can recover cosmological information beyond that captured by the pre-reconstruction summaries.
Reference Key
openalex_W7211887494 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anchal Saxena, P. Daniel Meerburg, Guochao Sun, Tzu‐Ching Chang, Lluís Mas-Ribas
Are you one of these authors? Claim your profile
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1972
URL
https://doi.org/10.1093/mnras/stag1972
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.