1. Exploring parameter spaces with artificial intelligence and machine learning black-box optimisation algorithms
- Author
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Fernando Abreu de Souza, Miguel Crispim Romão, Nuno Filipe Castro, Mehraveh Nikjoo, Werner Porod, and Universidade do Minho
- Subjects
High Energy Physics - Phenomenology ,High Energy Physics - Phenomenology (hep-ph) ,Science & Technology ,Ciências Naturais::Ciências Físicas ,Physics - Data Analysis, Statistics and Probability ,FOS: Physical sciences ,Computational Physics (physics.comp-ph) ,Physics - Computational Physics ,Data Analysis, Statistics and Probability (physics.data-an) - Abstract
Constraining Beyond the Standard Model theories usually involves scanning highly multi-dimensional parameter spaces and check observable predictions against experimental bounds and theoretical constraints. Such task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using Artificial Intelligence and Machine Learning search algorithms used for Black-Box optimisation problems. Using the cMSSM and the pMSSM parameter spaces, we consider both the Higgs mass and the Dark Matter Relic Density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency whilst reasonably covering the parameter space., We thank José Santiago Pérez and Jorge Romão for the careful reading of the paper draft and for the useful discussions. This work is supported by FCT - Fundação para a Ciência e a Tecnologia, I.P. under project CERN/FIS-PAR/0024/2019. FAS is also supported by FCT under the research grant with reference UI/BD/153105/2022. The computational work was partially done using the resources made available by RNCA and INCD under project CPCA/A1/401197/2021, info:eu-repo/semantics/publishedVersion
- Published
- 2023