Using Machine Learning to Select High-Quality Measurements

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.

8 pages, 3 figures

References in corpus (6)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Observation of Single Top Quark Production and Measurement of |Vtb| with CDF
• Muon decay in orbit: spectrum of high-energy electrons
• Bound muon decay spectrum in the leading logarithmic accuracy