Statistical model uncertainty and OPERA-like time-of-flight measurements
Oliver Riordan, Alex Selby

TL;DR
This paper discusses the importance of addressing model uncertainty in time-of-flight measurements like OPERA, proposing strategies to improve statistical analysis and reduce errors in experimental conclusions.
Contribution
It introduces and evaluates strategies for managing model uncertainty in timing experiments, suggesting more effective methods for analyzing OPERA-like data.
Findings
Averaging probability distributions can mitigate model uncertainty.
Incorrect implementation of averaging may introduce errors.
Proposed alternative strategies could improve accuracy in timing measurements.
Abstract
Time-of-flight measurements such as the OPERA and MINOS experiments rely crucially on statistical analysis (as well as many other ingredients) for their conclusions. The nature of these experiments leads to a simple class of statistical models for the results; however, which model in the class is appropriate is not known exactly, as this depends on information obtained experimentally, which is subject to noise and other errors. To obtain robust conclusions, this problem, known as "model uncertainty," needs to be addressed, with quantitative bounds on the effect such uncertainty may have on the final result. The OPERA (and MINOS) analysis appears to take steps to mitigate the effects of model uncertainty, though without quantifying any remaining effect. We describe one of the strategies used (averaging individual probability distributions), and point out a potential source of error if…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Frequency and Time Standards · Atomic and Subatomic Physics Research · Radioactive Decay and Measurement Techniques
