https://doi.org/10.1140/epjs/s11734-024-01236-w
Review
Probing intractable beyond-standard-model parameter spaces armed with machine learning
1
Department of Physics, SEAS, Bennett University, 201310, Greater Noida, Uttar Pradesh, India
2
Department of Physics, Bangabasi Evening College, 700009, Kolkata, West Bengal, India
3
Department of Physics, Bangabasi College, 700009, Kolkata, West Bengal, India
b
Subhadeep.Mondal@bennett.edu.in
Received:
1
April
2024
Accepted:
28
June
2024
Published online:
15
July
2024
This article attempts to summarize the effort by the particle physics community in addressing the tedious work of determining the parameter spaces of beyond-the-standard-model (BSM) scenarios, allowed by data. These spaces, typically associated with a large number of dimensions, especially in the presence of nuisance parameters, suffer from the curse of dimensionality and thus render naive sampling of any kind—even the computationally inexpensive ones—ineffective. Over the years, various new sampling (from variations of Markov Chain Monte Carlo (MCMC) to dynamic nested sampling) and machine learning (ML) algorithms have been adopted by the community to alleviate this issue. If not all, we discuss potentially the most important ones among them and the significance of their results, in detail.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
