Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms

Souza, Fernando Abreu de; Romão, Miguel Crispim; Castro, N. F.; Nikjoo, Mehraveh; Porod, W.

doi:10.48550/arxiv.2206.09223

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…In Ref. [21], the authors introduce dynamic sampling techniques for beyond the standard model searches.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Parameter Spaces Assisted by Machine Learning

Hammad¹,

Park²,

Ramos³

et al. 2022

Preprint

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We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The objective is the construction of routines that can be employed for different types of analysis, from finding bounds on the parameter space to accumulating samples in areas of interest. Moreover, for classification, we introduce a boosting technique to improve cases where the initial convergence is slow. The type of results that can be obtained is better explained with the help of a few examples from sampling simple toy models to exploring the parameter space of the two Higgs doublet model. The code used for this paper and instructions are publicly available on the web 1 .

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“…In Ref. [21], the authors introduce dynamic sampling techniques for beyond the standard model searches.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Parameter Spaces Assisted by Machine Learning

Hammad¹,

Park²,

Ramos³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Visualization and efficient generation of constrained high-dimensional theoretical parameter spaces

Baretz,

Carrara,

Hollingsworth

et al. 2023

J. High Energ. Phys.

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We describe a set of novel methods for efficiently sampling high-dimensional parameter spaces of physical theories defined at high energies, but constrained by experimental measurements made at lower energies. Often, theoretical models such as supersymmetry are defined by many parameters, $$ \mathcal{O} $$ O (10 − 100), expressed at high energies, while relevant experimental constraints are often defined at much lower energies, preventing them from directly ruling out portions of the space. Instead, the low-energy constraints define a complex, potentially non-contiguous subspace of the theory parameters. Naive scanning of the theory space for points which satisfy the low-energy constraints is hopelessly inefficient due to the high dimensionality, and the inverse problem is considered intractable. As a result, many theoretical spaces remain under-explored. We introduce a class of modified generative autoencoders, which attack this problem by mapping the high-dimensional parameter space to a structured low-dimensional latent space, allowing for easy visualization and efficient generation of theory points which satisfy experimental constraints. An extension without dimensional compression, which focuses on limiting potential information loss, is also introduced.

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Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms

Cited by 2 publications

References 43 publications

Exploration of Parameter Spaces Assisted by Machine Learning

Exploration of Parameter Spaces Assisted by Machine Learning

Visualization and efficient generation of constrained high-dimensional theoretical parameter spaces

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