Optimal Inflationary Potentials
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Abstract (arXiv)
Inflation is a highly favoured theory for the early Universe. It is compatible with current observations of the cosmic microwave background and large scale structure and is a driver in the quest to detect primordial gravitational waves. It is also, given the current quality of the data, highly under-determined with a large number of candidate implementations. We use a new method in symbolic regression to generate all possible simple scalar field potentials for one of two possible basis sets of operators. Treating these as single-field, slow-roll inflationary models we then score them with an information-theoretic metric ("minimum description length") that quantifies their efficiency in compressing the information in current data. We explore two possible priors on the parameter space of potentials, one related to the functions' structural complexity and one that uses a Katz back-off language model to prefer functions that may be theoretically motivated. This enables us to identify the inflaton potentials that optimally balance simplicity with accuracy at explaining current data, which may subsequently find theoretical motivation. Our exploratory study opens the door to extraction of fundamental physics directly from data, and may be augmented with more refined theoretical priors in the quest for a complete understanding of the early Universe.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 5 of its 40 references that are in the map. First posted on arXiv 25 Oct 2023.
Builds on
5 of 40 references.
References · 5
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
- 2022Rediscovering orbital mechanics with machine learningcited by 71
- 2020A Bayesian machine scientist to aid in the solution of challenging scientific problemscited by 61
- 2023Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical lawscited by 55
- 2023Priors for symbolic regressioncited by 10
Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 1.
6 papers.
- 2024Symbolic Regression for Beyond the Standard Model Physicscited by 5
- 2024Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Naturecited by 2
- 2025(Exhaustive) Symbolic Regression and model selection by minimum description lengthcited by 2
- 2025Symbolic Regression and Differentiable Fits in Beyond the Standard Model Physicscited by 1
- 2025Constraining dark matter halo profiles with symbolic regressioncited by 0
- 2026Symbolic Classification-Enabled LHC Limits for BSM Global Fitscited by 0
- 2026Symbolic Classification-Enabled LHC Limits for BSM Global Fits21 May 2026
- 2025Constraining dark matter halo profiles with symbolic regression28 Nov 2025
- 2025Symbolic Regression and Differentiable Fits in Beyond the Standard Model Physics23 Oct 2025
- 2025(Exhaustive) Symbolic Regression and model selection by minimum description length17 Jul 2025
- 2024Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature12 Aug 2024
- 2024Symbolic Regression for Beyond the Standard Model Physics28 May 2024