Spectral model selection in the electronic measurement of the Boltzmann constant by Johnson noise thermometry

TL;DR

This paper presents a spectral model selection method for precise determination of the Boltzmann constant via Johnson noise thermometry, emphasizing the impact of model complexity and bandwidth choice on measurement accuracy.

Contribution

It introduces a cross-validation approach to select the spectral model complexity and bandwidth, improving uncertainty quantification in Boltzmann constant measurements.

Findings

Model correctly identifies spectrum complexity in simulations

Bandwidth selection minimizes measurement uncertainty

Evidence of temporal trend in offset parameters

Abstract

In the electronic measurement of the Boltzmann constant based on Johnson noise thermometry, the ratio of the power spectral densities of thermal noise across a resistor at the triple point of water, and pseudo-random noise synthetically generated by a quantum-accurate voltage-noise source is constant to within 1 part in a billion for frequencies up to 1 GHz. Given this ratio, and the values of other known or measured parameters, one can determine the Boltzmann constant. Due, in part, to mismatch between transmission lines, the experimental ratio spectrum varies with frequency. We model this spectrum as an even polynomial function of frequency where the constant term in the polynomial determines the Boltzmann constant. When determining this constant (offset) from experimental data, the assumed complexity of the ratio spectrum model and the maximum frequency analyzed (fitting bandwidth) dramatically affects results. We select the complexity of the model by cross-validation. For each of many fitting bandwidths, we determine the component of uncertainty of the offset term that accounts for random and systematic effects associated with imperfect knowledge of model complexity. We select the fitting bandwidth that minimizes this uncertainty. In the most recent measurement of the Boltzmann constant, results were determined, in part, by application of an earlier version of the method described here. Here, we extend the earlier analysis by considering a broader range of fitting bandwidths and quantify an additional component of uncertainty that accounts for imperfect performance of our fitting bandwidth selection method. For idealized simulated data our method correctly selects the true complexity of the ratio spectrum model for all cases considered. A new analysis of data from the recent experiment yields evidence for a temporal trend in the offset parameters.