How accurate can combined measurements be -- experiment, simulation, and theory

TL;DR

This paper explores how combining multiple measurements of different mass combinations can significantly improve accuracy, supported by experiments, simulations, and analytical formulas, with broad applications in metrology.

Contribution

It introduces a general analytical expression for measurement error and demonstrates how combined measurements can vastly surpass individual scale resolution.

Findings

Combined measurements can estimate weights with errors much smaller than scale resolution.

Simulations and analytical models show error decreases with more measurements.

The approach is applicable to various metrological fields beyond mass measurement.

Abstract

In this paper we investigate the question of how much combined measurements can increase the accuracy of additive quantities. Therefore, we consider a set of measurements from a selection of all possible combinations of the $n$ labeled masses and then estimate the individual weights of the $n$ masses by a linear regression approach. We present experimental results which motivate comprehensive simulation campaigns. These simulations provide valid statistical statements and reliable forecasts of the experimental results. A profound analytical treatment in turn supports these simulation outcomes with excellent consistency. One important achievement therein is a general analytical expression for the estimate's error, not only limited to the two particular weighing schemes presented. It turns out that combined measurements allow to estimate the weight of mass elements with an accuracy that under-runs by orders of magnitude the resolution of the scale used. As the error depends on the amount of measurements, one gains higher accuracy with increasing effort. In a broader sense, our work wants to promote the method and give inspirations to applications in various metrological fields beyond high-precision mass determination. Moreover, the novel simulations and analytic formulas enable the design of optimal experiments.