Modelling geometric and dynamical observables with machine learning

Clicks: 1
ID: 317370
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

Ranked #808 of 908 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 908 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 We present a physically coupled inverse-emulation framework for the joint reconstruction of cosmic expansion and linear growth dynamics using observational H(z) and fσ8(z) data. The methodology combines a unified forward model linking expansion history, comoving geometry, Alcock–Paczynski distortions, and perturbation growth with machine-learning inverse emulators trained on physically generated realizations. Reference Bayesian inference is performed through Markov Chain Monte Carlo sampling, while robustness is examined using bootstrap resampling, K-fold cross-validation, observational jitter propagation, and prior-volume analyses. We show that the inverse-emulation models successfully recover both posterior parameter constraints and the dominant geometry–growth coupling structure of the Bayesian posterior. In particular, the reconstructed expansion and growth trajectories remain remarkably stable across independent machine-learning architectures, indicating that the learned inverse-emulation space preserves the underlying physical coupling between geometry and structure growth. The most stable reconstructions emerge in the Alcock–Paczynski geometric sector, whereas the largest deviations appear along higher-order kinematic degeneracy directions. These results demonstrate that machine-learning cosmological inference can move beyond phenomenological regression toward physically interpretable reconstruction of coupled geometry–growth dynamics in observable space.
Reference Key
openalex_W7164762809 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cihan Kömürcü, Can Aktaş
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1134
URL
https://doi.org/10.1093/mnras/stag1134
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.