Journal article
A precise symbolic emulator of the linear matter power spectrum
- Abstract:
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Context. Computing the matter power spectrum, P(k), as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for modern applications, so black-box, uninterpretable emulators are often used.
Aims. We aim to construct an efficient, differentiable, interpretable, symbolic emulator for the redshift zero linear matter power spectrum which achieves sub-percent level accuracy. We also wish to obtain a simple analytic expression to convert As to σ8 given the other cosmological parameters.
Methods. We utilise an efficient genetic programming based symbolic regression framework to explore the space of potential mathematical expressions which can approximate the power spectrum and σ8. We learn the ratio between an existing low-accuracy fitting function for P(k) and that obtained by solving the Boltzmann equations and thus still incorporate the physics which motivated this earlier approximation.
Results. We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0.2% between k = 9 × 10−3 − 9 h Mpc−1 and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression. Our analytic approximation is 950 times faster to evaluate than CAMB and 36 times faster than the neural network based matter power spectrum emulator BACCO. We also provide a simple analytic approximation for σ8 with a similar accuracy, with a root mean squared fractional error of just 0.1% when evaluated across the same range of cosmologies. This function is easily invertible to obtain As as a function of σ8 and the other cosmological parameters, if preferred.
Conclusions. It is possible to obtain symbolic approximations to a seemingly complex function at a precision required for current and future cosmological analyses without resorting to deep-learning techniques, thus avoiding their black-box nature and large number of parameters. Our emulator will be usable long after the codes on which numerical approximations are built become outdated.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.2MB, Terms of use)
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- Publisher copy:
- 10.1051/0004-6361/202348811
Authors
- Funder identifier:
- https://ror.org/03wnrjx87
- Grant:
- RGS\R1\221167
- Publisher:
- EDP Sciences
- Journal:
- Astronomy and Astrophysics More from this journal
- Volume:
- 686
- Article number:
- a209
- Publication date:
- 2024-06-12
- Acceptance date:
- 2024-04-03
- DOI:
- EISSN:
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1432-0746
- ISSN:
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0004-6361
- Language:
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English
- Keywords:
- Pubs id:
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2008394
- Local pid:
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pubs:2008394
- Deposit date:
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2024-11-06
Terms of use
- Copyright holder:
- Bartlett et al.
- Copyright date:
- 2024
- Rights statement:
- © The Authors 2024. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Notes:
- For the purposes of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
- Licence:
- CC Attribution (CC BY)
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