Improved methods for determining the kinematics of coronal mass ejections and coronal waves

TL;DR

This paper introduces improved, statistically rigorous methods for determining the kinematics of coronal mass ejections and coronal waves, emphasizing the use of the Savitzky-Golay filter and bootstrap techniques for more accurate and reliable estimates.

Contribution

It presents novel application of the Savitzky-Golay filter and bootstrap resampling to enhance the accuracy and robustness of CME and coronal wave kinematic measurements.

Findings

Savitzky-Golay filter improves fitting accuracy for CME kinematics

Bootstrap methods provide reliable error estimates and goodness-of-fit assessments

Enhanced measurement precision and sampling improve kinematic determination

Abstract

The study of solar eruptive events and associated phenomena is of great importance in the context of solar and heliophysics. Coronal mass ejections (CMEs) and coronal waves are energetic manifestations of the restructuring of the solar magnetic field and mass motion of the plasma. Characterising this motion is vital for deriving the dynamics of these events and thus understanding the physics driving their initiation and propagation. The development and use of appropriate methods for measuring event kinematics is therefore imperative. Traditional approaches to the study of CME and coronal wave kinematics do not return wholly accurate nor robust estimates of the true event kinematics and associated uncertainties. We highlight the drawbacks of these approaches, and demonstrate improved methods for accurate and reliable determination of the kinematics. The Savitzky-Golay filter is demonstrated as a more appropriate fitting technique for CME and coronal wave studies, and a residual resampling bootstrap technique is demonstrated as a statistically rigorous method for the determination of kinematic error estimates and goodness-of-fit tests. It is shown that the scatter on distance-time measurements of small sample size can significantly limit the ability to derive accurate and reliable kinematics. This may be overcome by (i) increasing measurement precision and sampling cadence, and (ii) applying robust methods for deriving the kinematics and reliably determining their associated uncertainties. If a priori knowledge exists and a pre-determined model form for the kinematics is available (or indeed any justified fitting-form to be tested against the data), then its precision can be examined using a bootstrapping technique to determine the confidence interval associated with the model/fitting parameters.