Using machine learning to optimise chameleon fifth force experiments

Briddon, Chad; Burrage, Clare; Moss, Adam; Tamosiunas, Andrius

Abstract

The chameleon is a theorised scalar field that couples to matter and possess a screening mechanism, which weakens observational constraints from experiments performed in regions of higher matter density. One consequence of this screening mechanism is that the force induced by the field is dependent on the shape of the source mass (a property that distinguishes it from gravity). Therefore an optimal shape must exist for which the chameleon force is maximised. Such a shape would allow experiments to improve their sensitivity by simply changing the shape of the source mass. In this work we use a combination of genetic algorithms and the chameleon solving software SELCIE to find shapes that optimise the force at a single point in an idealised experimental environment. We note that the method we used is easily customised, and so could be used to optimise a more realistic experiment involving particle trajectories or the force acting on an extended body. We find the shapes outputted by the genetic algorithm possess common characteristics, such as a preference for smaller source masses, and that the largest fifth forces are produced by small `umbrella'-like shapes with a thickness such that the source is unscreened but the field reaches its minimum inside the source. This remains the optimal shape even as we change the chameleon potential, and the distance from the source, and across a wide range of chameleon parameters. We find that by optimising the shape in this way the fifth force can be increased by 2.45 times when compared to a sphere, centred at the origin, of the same volume and mass.

Citation

Briddon, C., Burrage, C., Moss, A., & Tamosiunas, A. (2024). Using machine learning to optimise chameleon fifth force experiments. Journal of Cosmology and Astroparticle Physics, 2024(2), Article 11. https://doi.org/10.1088/1475-7516/2024/02/011

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2023
• Online Publication Date: Feb 5, 2024
• Publication Date: 2024-02
• Deposit Date: Mar 25, 2024
• Publicly Available Date: Mar 26, 2024
• Journal: Journal of Cosmology and Astroparticle Physics
• Electronic ISSN: 1475-7516
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 2024
• Issue: 2
• Article Number: 11
• DOI: https://doi.org/10.1088/1475-7516/2024/02/011
• Keywords: dark energy theory, modified gravity, Machine learning
• Public URL: https://nottingham-repository.worktribe.com/output/31435314
• Publisher URL: https://iopscience.iop.org/article/10.1088/1475-7516/2024/02/011
• Additional Information: Article Title: Using machine learning to optimise chameleon fifth force experiments; Journal Title: Journal of Cosmology and Astroparticle Physics; Article Type: paper; Copyright Information: © 2024 The Author(s); Date Received: 2023-08-11; Date Accepted: 2023-12-30; Online publication date: 2024-02-05