MFV approach to robust estimate of neutron lifetime

Abstract

Aiming at evaluating the lifetime of the neutron, we introduce a novel statistical method to analyse the updated compilation of precise measurements including the 2022 dataset of Particle Data Group (PDG). Based on the minimization for the information loss principle, unlike the median statistics method, we apply the most frequent value (MFV) procedure to estimate the neutron lifetime, irrespective of the Gaussian or non-Gaussian distributions. Providing a more robust way, the calculated result of the MFV is ${\tau }_n=881.16_{-2.35}^{+2.25}$ s with statistical bootstrap errors, while the result of median statistics is ${\tau }_n=881.5_{-3}^{+5.5}$ s according to the binomial distribution. Using the different central estimates, we also construct the error distributions of neutron lifetime measurements and find the non-Gaussianity, which is still meaningful.

References in corpus (4)

• Clustering of Local Group distances: publication bias or correlated measurements? I. The Large Magellanic Cloud
• Not Normal: the uncertainties of scientific measurements
• Median Statistics Estimate of the Distance to the Galactic Center
• Median statistics estimates of Hubble and Newton's Constant

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