Data-Driven Uncertainty Quantification of the Wave-Telescope Technique: General Equations and Application to HelioSwarm

TL;DR

This paper develops a Bayesian-based uncertainty quantification method for the wave-telescope technique, enhancing wavevector reconstruction accuracy for multi-spacecraft missions like HelioSwarm.

Contribution

It introduces generalized equations for error estimation applicable to 4-9 spacecraft configurations, improving wave analysis in space plasma measurements.

Findings

Error equations accurately predict wavevector reconstruction errors.

Application to HelioSwarm data improves wavevector characterization.

Method extends to arbitrary spacecraft configurations.

Abstract

The upcoming NASA mission HelioSwarm will use nine spacecraft to make the first simultaneous multi-point measurements of space plasmas spanning multiple scales. Using the wave-telescope technique, HelioSwarm's measurements will allow for both the calculation of the power in wavevector-and-frequency space and the characterization of the associated dispersion relations of waves present in the plasma at MHD and ion-kinetic scales. This technique has been applied to the four-spacecraft missions of CLUSTER and MMS and its effectiveness has previously been characterized in a handful of case studies. We expand this uncertainty quantification analysis to arbitrary configurations of four through nine spacecraft for three-dimensional plane waves. We use Bayesian inference to learn equations that approximate the error in reconstructing the wavevector as a function of relative wavevector magnitude, spacecraft configuration shape, and number of spacecraft. We demonstrate the application of these equations to data drawn from a nine-spacecraft configuration to both improve the accuracy of the technique, as well as expand the magnitudes of wavevectors that can be characterized.