Bayesian and Deterministic Neural Network approaches to Faraday Cup calibration and plasma parameter estimation

TL;DR

This paper introduces a neural network-based method for calibrating particle detectors and estimating plasma parameters that is robust to calibration errors, demonstrated on spacecraft data with potential applications in space missions.

Contribution

It presents a novel calibration scheme using neural networks trained with dynamic time warping, effective even with poorly calibrated data, for plasma parameter estimation.

Findings

Neural network approach accurately estimates plasma parameters.

Method is insensitive to calibration errors in measurements.

Demonstrated on DSCOVR and Wind spacecraft data.

Abstract

We describe a novel scheme for analyzing particle detector measurements when a well-calibrated, similarly instrumented spacecraft is present in a similar orbit. To prepare ground truth from measurements provided by a reference spacecraft, the method uses dynamic time warping (DTW)--a technique often used for pattern-matching in time series data. An artificial neural network (ANN) is created and trained to reproduce this ground truth from measurements at the target spacecraft. Unlike previous approaches, this procedure is insensitive to calibration errors in the target data stream, as the neural network may be trained from poorly calibrated particle spectra or even directly from low-level data in engineering units. We demonstrate a proof-of-concept by training an ANN to estimate solar wind proton densities, temperatures, and speeds from the DSCOVR PlasMag Faraday Cup, using the \textit{Wind} Solar Wind Experiment as a reference. We present both deterministic and Bayesian neural network approaches. Applications for Parker Solar Probe, HelioSwarm, and other missions are discussed.