Using Machine Learning to Select High-Quality Measurements

TL;DR

This paper presents a machine learning approach to identify high-quality measurements in the Mu2e experiment, aiming to improve data accuracy by filtering out measurements affected by errors.

Contribution

It introduces a novel machine learning-based method that leverages ancillary information to distinguish high-quality measurements from low-quality ones in experimental data.

Findings

Effective separation of measurement quality achieved

Improved data reliability for Mu2e experiment

Potential application to other experiments with measurement errors

Abstract

We describe the use of machine learning algorithms to select high-quality measurements for the Mu2e experiment. This technique is important for experiments with backgrounds that arise due to measurement errors. The algorithms use multiple pieces of ancillary information that are sensitive to measurement quality to separate high-quality and low-quality measurements.