English

COBRA: Optimal Factorization of Cosmological Observables

Cosmology and Nongalactic Astrophysics 2025-04-15 v2

Abstract

We introduce COBRA (Cosmology with Optimally factorized Bases of Radial Approximants), a novel framework for rapid computation of large-scale structure observables. COBRA separates scale dependence from cosmological parameters in the linear matter power spectrum while also minimising the number of necessary basis terms NbN_b, thus enabling direct and efficient computation of derived and nonlinear observables. Moreover, the dependence on cosmological parameters is efficiently approximated using radial basis function interpolation. We apply our framework to decompose the linear matter power spectrum in the standard Λ\LambdaCDM scenario, as well as by adding curvature, dynamical dark energy and massive neutrinos, covering all redshifts relevant for Stage IV surveys. With only a dozen basis terms NbN_b, COBRA reproduces exact Boltzmann solver calculations to 0.1%\sim 0.1\% precision, which improves further to 0.02%0.02\% in the pure Λ\LambdaCDM scenario. Using our decomposition, we recast the one-loop redshift space galaxy power spectrum in a separable minimal-basis form, enabling 4000\sim 4000 model evaluations per second at 0.02%0.02\% precision on a single thread. This constitutes a considerable improvement over previously existing methods (e.g., FFTLog) opening a window for efficient computations of higher loop and higher order correlators involving multiple powers of the linear matter power spectra. The resulting factorisation can also be utilised in clustering, weak lensing and CMB analyses. Our implementation will be made public upon publication.

Keywords

Cite

@article{arxiv.2407.04660,
  title  = {COBRA: Optimal Factorization of Cosmological Observables},
  author = {Thomas Bakx and Nora Elisa Chisari and Zvonimir Vlah},
  journal= {arXiv preprint arXiv:2407.04660},
  year   = {2025}
}

Comments

5+6 pages, substantial rewriting, conclusions unchanged. PRL accepted version

R2 v1 2026-06-28T17:30:34.300Z