2018/03/02 by Georg Schnabel, Schnabel, Georg
Engineering · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Nuclear Experiment (nucl-ex) #Nuclear Physics and Applications #Nuclear Theory (nucl-th) #Nuclear physics research studies #Nuclear reactor physics and engineering #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1803.00960
openalex publication_date 2018/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Consistent experiment data are crucial to adjust parameters of physics models and to determine best estimates of observables. However, often experiment data are not consistent due to unrecognized systematic errors. Standard methods of statistics such as χ2-fitting cannot deal with this case. Their predictions become doubtful and associated uncertainties too small. A human has then to figure out the problem, apply corrections to the data, and repeat the fitting procedure. This takes time and potentially costs money. Therefore, a Bayesian method is introduced to fit and analyze inconsistent experiment data. It automatically detects and resolves inconsistencies. Furthermore, it allows to extract consistent subsets from the data. Finally, it provides an overall prediction with associated uncertainties and correlations less prone to the common problem of too small uncertainties. The method is foreseen to function with a large corpus of data and hence may be used in nuclear databases to deal with inconsistencies in an automated fashion.