Limitations from charge quantization on the parallel temperature diagnostic of nonneutral plasmas

TL;DR

This paper introduces a new maximum likelihood algorithm for estimating the temperature of nonneutral plasmas, effective even with low counts, crucial for antihydrogen trapping applications.

Contribution

The paper presents a novel algorithm that accurately estimates plasma temperature from minimal charge data, improving analysis of low-temperature plasmas.

Findings

Requires about 100 counts for 10% accuracy

Effective for temperatures down to approximately 3 K

Robust against shot noise and external noise

Abstract

We develop a new algorithm to estimate the temperature of a nonneutral plasma in a Penning-Malmberg trap. The algorithm analyzes data obtained by slowly lowering a voltage that confines one end of the plasma and collecting escaping charges, and is a maximum likelihood estimator based on a physically-motivated model of the escape protocol presented in Beck [1990]. Significantly, our algorithm may be used on single-count data, allowing for improved fits with low numbers of escaping electrons. This is important for low-temperature plasmas such as those used in antihydrogen trapping. We perform a Monte Carlo simulation of our algorithm, and assess its robustness to intrinsic shot noise and external noise. Approximately 100 particle counts are needed for an accuracy of +/-10% -- this provides a lower bound for measurable plasma temperatures of approximately 3 K for plasmas of length 1 cm.