Detecting and Characterising Small-Scale Brightenings in Solar Imaging Data

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Abstract

Observations of small-scale brightenings in the low solar atmosphere can provide valuable constraints on possible heating and heat transport mechanisms. We present a method for the detection and analysis of brightenings, and demonstrate its application to time-series imagery of the Interface Region Imaging Spectrograph (IRIS) in the extreme ultraviolet (EUV). The method is based on spatio-temporal band-pass filtering, adaptive thresholding and centroid tracking, and records an event’s spatial position, duration, total brightness and maximum brightness. Spatial area, brightness, and position are also recorded as functions of time throughout the event’s lifetime. Detected brightenings can fragment, or merge, over time – thus the number of distinct regions constituting a brightening event is recorded over time, and the maximum number of regions recorded as Nfrag, which is a simple measure of an event’s coherence or spatial complexity. A test is made on a synthetic datacube composed of a static background based on IRIS data, Poisson noise and ≈ 10 4 randomly-distributed, moving, small-scale Gaussian brightenings. Maximum brightness, total brightness, area, and duration follow power-law distributions, and the results show the range over which the method can successfully extract information. The test shows that the recorded maximum brightness of an event is a reliable measure for the brightest and most accurately detected events, with an error of 6%. Event area, duration and speed are generally underestimated by around 15% and have an uncertainty of 20–30%. The total brightness is underestimated by 30%, and has an uncertainty of 30%. Applying this detection method to real IRIS quiet-sun data spanning 19 minutes over a 54.40 × 55.23 field of view (FOV) yields 2997 detections, 1340 of these detections either remain un-fragmented or fragment to two distinct regions at least once during their lifetime (Nfrag≤ 2), equating to an event density of 3.96 × 10 − 4 arcsec−2 s−1. The method will be used for a future large-scale statistical analysis of several quiet-sun (QS) data sets from IRIS, other EUV imagers, and other types of data including Hα and visible photospheric imagery.

Original languageEnglish
Article number140
JournalSolar Physics
Volume296
Issue number9
DOIs
Publication statusPublished - 29 Sept 2021

Keywords

  • Methods: data analysis
  • Methods: observational
  • Sun: activity
  • Sun: transition region
  • Techniques: image processing

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