Combining evolutionary strategies and novelty detection to go beyond the alignment limit of the Z3 3HDM

Crispim Romão, Jorge; Crispim Romão, Miguel

Abstract

We present a novel artificial intelligence approach for beyond the Standard Model parameter space scans by augmenting an evolutionary strategy with novelty detection. Our approach leverages the power of evolutionary strategies, previously shown to quickly converge to the valid regions of the parameter space, with a novelty reward to continue exploration once converged. Taking the Z3 3HDM as our physics case, we show how our methodology allows us to quickly explore highly constrained multidimensional parameter spaces, providing up to eight orders of magnitude higher sampling efficiency when compared with pure random sampling and up to four orders of magnitude when compared to random sampling around
the alignment limit. In turn, this enables us to explore regions of the parameter space that have been hitherto overlooked, leading to the possibility of novel phenomenological realizations of the Z3 three Higgs doublet model that had not been considered before.

Citation

Crispim Romão, J., & Crispim Romão, M. (2024). Combining evolutionary strategies and novelty detection to go beyond the alignment limit of the Z3 3HDM. Physical Review D, 109(9), Article 095040. https://doi.org/10.1103/physrevd.109.095040

• Journal Article Type: Article
• Acceptance Date: Apr 23, 2024
• Online Publication Date: May 24, 2024
• Publication Date: May 24, 2024
• Deposit Date: Jul 8, 2024
• Publicly Available Date: Jul 8, 2024
• Journal: Physical Review D
• Print ISSN: 2470-0010
• Electronic ISSN: 2470-0029
• Publisher: American Physical Society
• Peer Reviewed: Peer Reviewed
• Volume: 109
• Issue: 9
• Article Number: 095040
• DOI: https://doi.org/10.1103/physrevd.109.095040
• Public URL: https://durham-repository.worktribe.com/output/2521791