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F. Feroz, M. P. Hobson and M. Bridges, “MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics,” Mon. Not. Roy. Astron. Soc. 398
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P. Bechtle, O. Brein, S. Heinemeyer, G. Weiglein and K. E. Williams, “HiggsBounds 2.0.0: Confronting Neutral and Charged Higgs Sector Predictions with Exclusion Bounds from LEP and the Tevatron,” Comput. Phys. Commun. 182
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2022

Exploration of Parameter Spaces Assisted by Machine Learning

Hammad, A., Park, Myeonghun, Ramos, Raymundo et al.

Understand

We demonstrate two sampling procedures assisted by machine learning models via regression and classification.

The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations.
We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better.
In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations.