Symbolic emulators for cosmology: accelerating cosmological analyses without sacrificing precision

Journal article

In cosmology, emulators play a crucial role by providing fast and accurate predictions of complex physical models, enabling efficient exploration of high-dimensional parameter spaces that would be computationally prohibitive with direct numerical simulations. Symbolic emulators have emerged as promising alternatives to numerical approaches, delivering comparable accuracy with significantly faster evaluation times. While previous symbolic emulators were limited to relatively narrow prior ranges, we expand these to cover the parameter space relevant for current cosmological analyses. We introduce approximations to hypergeometric functions used for the Λ cold dark matter (ΛCDM) comoving distance and linear growth factor which are accurate to better than 0.001% and 0.05%, respectively, for all redshifts and for Ωm∈[0.1, 0.5]. We show that integrating symbolic emulators into a Dark Energy Survey Year 1 (DES-Y1)-like 3×2 pt analysis produces cosmological constraints consistent with those obtained using standard numerical methods. Our symbolic emulators offer substantial improvements in speed and memory usage, demonstrating their practical potential for scalable, likelihood-based inference. This article is part of the discussion meeting issue ‘Symbolic regression in the physical sciences’.

Authors: Bartlett, D; Pandey, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: The Royal Society
• Journal: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
• Volume: 384
• Issue: 2317
• Pages: 20240585
• Article number: 20240585
• Publication date: 2026-04-09
• Acceptance date: 2025-11-17
• DOI: 10.1098/rsta.2024.0585
• EISSN: 1471-2962
• ISSN: 1364503X, 1364-503X
• Language: English
• Keywords: large-scale structure; machine learning; symbolic regression; weak lensing; power spectrum; cosmological parameters