# Using Machine Learning to Select High-Quality Measurements

arXiv: https://arxiv.org/abs/2106.08891 · HTML: https://mlsanity.com/paper/2106.08891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 20 references that are in the map.
- First posted on arXiv 28 May 2021.

## Abstract (arXiv)

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.
