Using Machine Learning to Select High-Quality Measurements
This work addresses measurement quality issues in particle physics experiments like Mu2e, but it appears incremental as it applies existing ML methods to a specific domain without claiming major breakthroughs.
The authors tackled the problem of selecting high-quality measurements for the Mu2e experiment by using machine learning algorithms that leverage ancillary information to separate high- and low-quality data, resulting in improved selection efficiency.
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.