2017/03/03 by P. Koppenburg, Koppenburg, Patrick · 1 citation
Computer Science · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #High Energy Physics - Experiment (hep-ex) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1703.01128
openalex publication_date 2017/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many measurements at collider experiments study physics candidates that are a subset of a collision event. The presence of multiple such candidates in a given event can cause raw biases which are large compared to typical statistical uncertainties. Selecting a single candidate is common practice but only helps if the likelihood of selecting the true candidate is very high. Otherwise the precision of the measurement can be affected, and biases can be generated, even if none are present in the data sample prior to this operation. This paper aims at describing the problem in a systematic way. It sets definitions, provides examples of potential biases using pseudoexperiments and gives recommendations.