Exploration of Parameter Spaces Assisted by Machine Learning

TL;DR

This paper introduces machine learning-assisted sampling methods, including neural network-based regression and classification, to efficiently explore parameter spaces, demonstrating comparable or improved results over traditional sampling techniques.

Contribution

It presents novel ML-assisted sampling procedures using neural networks, boosting techniques, and compares them with established methods like MCMC and MultiNest.

Findings

ML methods achieve similar or better sampling efficiency

Boosting enhances classifier performance rapidly

Applicable to toy models and complex physics models

Abstract

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. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.