Uncertainty Aware Learning for High Energy Physics

TL;DR

This paper explores an uncertainty-aware machine learning approach in High Energy Physics that leverages systematic uncertainties to improve sensitivity in parameter estimation, outperforming traditional invariant strategies.

Contribution

It introduces a novel classifier that incorporates uncertainty and nuisance parameters, demonstrating improved sensitivity in HEP data analysis.

Findings

Uncertainty-aware classifiers outperform traditional methods.

Enhanced sensitivity achieved with the proposed approach.

Validated on both synthetic and realistic HEP datasets.

Abstract

Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced sensitivity to systematic uncertainties. Contrary to the traditional wisdom of constructing an analysis strategy that is invariant to systematic uncertainties, we study the use of a classifier that is fully aware of uncertainties and their corresponding nuisance parameters. We show that this dependence can actually enhance the sensitivity to parameters of interest. Studies are performed using a synthetic Gaussian dataset as well as a more realistic HEP dataset based on Higgs boson decays to tau leptons. For both cases, we show that the uncertainty aware approach can achieve a better sensitivity than alternative machine learning strategies.