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SYREN-NEW: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy

Ce Sui, Deaglan J. Bartlett, Shivam Pandey, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt · 2025 · 15 citationsRead the paper

Context. Current and future large-scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. To do this efficiently, rapid yet accurate evaluation of the matter power spectrum in the presence of these effects is essential. Aims. We aim to construct accurate and interpretable symbolic approximations of the linear and nonlinear matter power spectra as a function of cosmological parameters in extended ΛCDM models that contain massive neutrinos and nonconstant equations of state for dark energy. This constitutes an extension of the SYREN-HALOFIT emulators to incorporate these two effects, which we call SYREN-NEW (SYmbolic-Regression-ENhanced power spectrum emulator with NEutrinos and W0−wa). We also wish to obtain a simple approximation of the derived parameter, σ8, as a function of the cosmological parameters for these models. Methods. We utilizedd symbolic regression to efficiently search through candidate analytic expressions to approximate the various quantities of interest. Our results for the linear power spectrum are designed to emulate CLASS, whereas for the nonlinear case we aim to match the results of EUCLIDEMULATOR2. We compared our results to existing emulators and N-body simulations. Results. Our analytic emulators for σ8, and the linear and nonlinear power spectra achieve root mean squared errors of 0.1%, 0.3%, and 1.3%, respectively, across a wide range of cosmological parameters, redshifts and wavenumbers. The error on the nonlinear power spectrum is reduced by approximately a factor of 2 when considering observationally plausible dark energy models and neutrino masses. We verify that emulator-related discrepancies are subdominant compared to observational errors and other modeling uncertainties when computing shear power spectra for LSST-like surveys. Our expressions have similar accuracy to existing (numerical) emulators, but are at least an order of magnitude faster, both on a CPU and a GPU. Conclusions. Our work greatly improves the accuracy, speed, and applicability range of current symbolic approximations of the linear and nonlinear matter power spectra. These now cover the same range of cosmological models as many numerical emulators with similar accuracy, but are much faster and more interpretable. We provide publicly available code for all symbolic approximations found.

What the full text shows

Working prototype
  • Linear matter power spectrum emulator achieves root mean squared error (RMSE). — 0.3% (Across a wide range of cosmological parameters, redshifts, and wavenumbers)
  • Nonlinear matter power spectrum emulator achieves root mean squared error (RMSE). — 1.3% (Across a wide range of cosmological parameters, redshifts, and wavenumbers)
  • Nonlinear power spectrum error is reduced for observationally plausible models. — factor of 2 reduction (sub-percent agreement) (Cosmologies within the 9σ region of current cosmological constraints)
  • Derived parameter σ8 emulator achieves root mean squared error (RMSE). — 0.1% (As a function of As and other cosmological parameters)
  • Computational speed-up of linear power spectrum emulator vs. Boltzmann code on CPU. — 3 × 10^4 times faster (CPU execution)
  • Computational speed-up of linear power spectrum emulator vs. Boltzmann code on GPU. — 6 × 10^6 times faster (3e4 * 200) (GPU execution)
Setup
Validated against N-body simulations and numerical emulators (EUCLIDEMULATOR2) across various cosmological parameters, redshifts, and wavenumbers.
How
Used symbolic regression to find analytic expressions for corrections to existing physics-informed formulae for linear and nonlinear matter power spectra. The linear emulator targets CLASS results, while the nonlinear emulator targets EUCLIDEMULATOR2.
Limitations
Does not include effects of baryonic feedback, which are important on the smallest scales. · Trained on numerical emulators rather than directly on N-body simulations, potentially limiting performance if the intermediary emulator is inaccurate.

Summarized by Gemma 4 from the open-access full text · 8 Oct 2026. Read it