Response to "Comment on `Resolving the 180deg Ambiguity in Solar Vector Magnetic Field Data: Evaluating the Effects of Noise, Spatial Resolution, and Method Assumptions'"

TL;DR

This paper responds to critiques on magnetic field ambiguity resolution, confirming that resolution limits and model assumptions impact algorithm performance, and emphasizes careful categorization of analysis methods.

Contribution

It clarifies the effects of spatial resolution and model assumptions on ambiguity-resolution algorithms and discusses proper categorization of data-analysis methods.

Findings

Limited spatial resolution challenges algorithms

Method performance depends on model assumptions

Proper categorization of algorithms is crucial

Abstract

We address points recently discussed in Georgoulis (2011) in reference to Leka et al. (2009b). Most importantly, we find that the results of Georgoulis (2011) support a conclusion of Leka et al. (2009b): that limited spatial resolution and the presence of unresolved magnetic structures can challenge ambiguity- resolution algorithms. Moreover, the findings of both Metcalf et al. (2006) and Leka et al. (2009b) are confirmed in Georgoulis (2011): a method's performance can be diminished when the observed field fails to conform to that method's assumptions. The implication of boundaries in models of solar magnetic structures is discussed; we confirm that the distribution of the field components in the model used in Leka et al. (2009b) is closer to what is observed on the Sun than what is proposed in Georgoulis (2011). It is also shown that method does matter with regards to simulating limited spatial resolution and avoiding an inadvertent introduction of bias. Finally, the assignment of categories to data- analysis algorithms is revisited; we argue that assignments are only useful and elucidating when used appropriately.