A Center-Median Filtering Method for Detection of Temporal Variation in Coronal Images

TL;DR

This paper introduces a per-pixel median filtering method to detect and distinguish temporal variations in solar coronal images, improving the clarity of dynamic event identification like EIT Waves.

Contribution

It develops a novel technique based on median filtering and photon-counting statistics for better separation of solar coronal events by their timescales.

Findings

More effective discrimination of EIT Waves than traditional difference methods.

Ability to trace wave signals to the edge of the AIA field of view with rebinned data.

Reveals more signal in coronal data than previously recognized.

Abstract

Events in the solar corona are often widely separated in their timescales, which can allow them to be identified when they would otherwise be confused with emission from other sources in the corona. Methods for cleanly separating such events based on their timescales are thus desirable for research in the field. This paper develops a technique for identifying time-varying signals in solar coronal image sequences which is based on a per-pixel running median filter and an understanding of photon-counting statistics. Example applications to 'EIT Waves' and small-scale dynamics are shown, both using data from the 193 Angstrom channel on AIA. The technique is found to discriminate EIT Waves more cleanly than the running and base difference techniques most commonly used. It is also demonstrated that there is more signal in the data than is commonly appreciated, finding that the waves can be traced to the edge of the AIA field of view when the data are rebinned to increase the signal-to-noise ratio.