Using machine learning to select high-quality measurements. (9th August 2021)

• Record Type: Journal Article
• Authors: Edmonds, A.; Brown, D.; Vinas, L.; Pagan, S.
• Is Part Of: Journal of instrumentation. Volume 16:Number 8(2021)
• Publication Date: 2021-08-09
• Subjects: Analysis and statistical methods -- Particle tracking detectors -- Performance of High Energy Physics Detectors; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/16/08/T08010
• Languages: English
• ISSNs: 1748-0221

Abstract

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.