SPINE : Symbolic Models to Predict the Evolution of the ΛCDM Nonlinear Power Spectrum

Clicks: 13
ID: 327185
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
Steady

Ranked #1,044 of 1,339 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 1,339 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 SPINE and SPINEX, a pair of analytical emulators developed to predict the nonlinear power spectrum based on its linear counterpart and several essential cosmological parameters within the range of 0.01 h Mpc−1 < k < 2 h Mpc−1. The primary difference between the two models is their parameterisation. Our methodology is grounded in the mapping originally proposed by Peacock and Dodds (1996). Both models are defined by clear mathematical expressions derived from symbolic regression, a machine learning technique that utilises genetic programming to identify analytical equations that accurately represent the underlying data. This approach provides a more interpretable and efficient alternative to conventional numerical methods that should also exhibit superior extrapolation behaviour beyond the training range. The emulators have been trained on the ΛCDM Quijote Latin Hypercube simulations. We provide fits for both emulators across three distinct scenarios: cosmologies within 20σ of the Planck-2018 observations, a designated fiducial cosmology, and a broader range of cosmologies sampled from the Quijote simulations. Our findings indicate that SPINE and SPINEX maintain an accuracy of better than 5% in the majority of cases. These emulators provide a rapid alternative to numerical methods, and future initiatives will focus on developing expressions that incorporate galaxy bias and redshift-space distortions. This advancement aims to enhance the modelling of redshift space power spectra across multiple redshifts, enabling their application in large-scale cosmological surveys.
Reference Key
openalex_W7204835041 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Manvi Chauhan, Daniel J. Farrow, Ariel G. Sánchez, Kevin A. Pimbblet, Marika Asgari, David M. Benoit
Are you one of these authors? Claim your profile
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1647
URL
https://doi.org/10.1093/mnras/stag1647
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.