Automated Spatiotemporal Analysis of Fibrils and Coronal Rain using the Rolling Hough Transform

TL;DR

This paper introduces an automated method based on the Rolling Hough Transform for analyzing the orientation and apparent motion of solar features like fibrils and coronal rain in multidimensional imaging data, improving speed and reliability.

Contribution

It extends the RHT technique to operate on a pixel-by-pixel basis for detailed orientation mapping and develops a hierarchical approach for automatic motion analysis in time-series data.

Findings

Reliable quantification of feature orientation even in low signal-to-noise conditions.

Automatic derivation of apparent motion in coronal rain observations.

Effective filtering of results through new error analysis formulation.

Abstract

A technique is presented that automates the direction characterization of curvilinear features in multidimensional solar imaging data sets. It is an extension of the Rolling Hough Transform (RHT) technique presented by Clark, Peek, and Putman (Astrophys. J. 789, 82, 2014), and it excels at rapid quantification of spatial and spatiotemporal feature orientation even for low signal to noise applications. It operates on a pixel-by-pixel basis within a data set and reliably quantifies orientation even for locations not centered on a feature ridge, which is used here to derive a quasi-continuous map of the chromospheric fine structure projection angle. For time-series analysis, a procedure is developed that uses a hierarchical application of the RHT to automatically derive apparent motion of coronal rain observed off-limb. Essential to the success of this technique is this paper's formulation for the RHT error analysis as it provides a means to properly filter results.