Exploring the biases of a new method based on minimum variance for interplanetary magnetic clouds
Pascal D\'emoulin, Sergio Dasso, Miho Janvier

TL;DR
This paper investigates biases in the minimum variance method for modeling interplanetary magnetic clouds, identifies their origins, and proposes an improved approach to reduce these biases for more accurate flux rope axis determination.
Contribution
The authors analyze the biases of the MV method, identify their sources, and develop an improved version that reduces bias by balancing magnetic flux and optimizing boundary selection.
Findings
MV eigenvalue ratio is unreliable for axis precision
Boundary selection significantly affects axis orientation
Improved MV reduces bias to less than 6 degrees
Abstract
Magnetic clouds (MCs) are twisted magnetic structures ejected from the Sun and probed by in situ instruments. They are typically modeled as flux ropes (FRs). The determination of the FR global characteristics requires the estimation of the FR axis orientation. Among the developed methods, the minimum variance (MV) is the most flexible, and features only a few assumptions. However, as other methods, MV has biases. We aim to investigate the limits of the method and extend it to a less biased method. We first identified the origin of the biases by testing the MV method on cylindrical and elliptical models with a temporal expansion comparable to the one observed in MCs. Then, we developed an improved MV method to reduce these biases. In contrast with many previous publications we find that the ratio of the MV eigenvalues is not a reliable indicator of the precision of the derived FR axis…
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