Information-Loss Location Estimation and Curvature-Scale Analysis of Physical Measurements: A Gaussian, Cauchy, and Logistic Neutron-Lifetime Benchmark

Abstract

We develop and implement a two-step location-scale analysis for repeated physical measurements when the underlying distribution is not known. For a chosen substitute distribution, stationarity of population Kullback-Leibler information loss at fixed scale motivates the location score, which we apply to the observed measurements through empirical cross-entropy. Steiner's curvature rule then defines a companion scale separately. We place Gaussian, Cauchy, and logistic substitutes in one common construction and implement them reproducibly on a physics benchmark. For the logistic substitute, we pair the established bounded location score with a scale defined by the curvature rule rather than by a fitted tuning constant or a scale-likelihood equation. For Gaussian measurements with different quoted uncertainties, an experiment-level curvature convention and a stated variance mapping give an exact algebraic connection to a standard residual-based variance estimate for a weighted fit. Applied to a published 21-measurement neutron-lifetime compilation, the inverse-variance weighted, most frequent value, and logistic locations are 878.689, 881.164, and 882.835 s, respectively. These values are benchmark results of the compared constructions and are not proposed as a new recommended neutron lifetime.