Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics

Abstract

High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H\(\alpha \) chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed.

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Notes

  1. 1.

    ssw/package/mjastereo/idl/looptracing auto4.pro.

  2. 2.

    https://svs.gsfc.nasa.gov/3913.

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Acknowledgements

The authors would like to thank the International Space Science Institute (ISSI) in Bern, Switzerland, for the hospitality provided to the members of the team on “The Nature and Physics of Vortex Flows in Solar Plasmas”. We acknowledge support of this work by the project “PROTEAS II” (MIS 5002515), which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). The Swedish 1-m Solar Telescope is operated on the island of La Palma by the Institute for Solar Physics of Stockholm University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias.

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Appendices

Appendix A: Flow Chart of the Method

A schematic representation of the proposed automated chromospheric swirl detection method is shown in the following flow chart (Figure 13).

Figure 13
figure13

Flow chart of the automated chromospheric swirl detection method.

Appendix B: Derivation of Curvature Criteria

Considering a certain segment, the algorithm calculates the directional angle for each point of a segment:

$$ \alpha _{j}=\alpha _{j-1}+\Delta \alpha _{j}, $$
(10)

with \(j=1,\ldots,N_{L}\), where \(N_{L}\) is the number of points of the segment, \(\alpha_{0}\) is the angle of the first segment point that results from the tracing algorithm, \(\Delta \alpha _{j}=\frac{\Delta S}{r_{j}}\) is the local curvature angle, \(\Delta S\) is the length of the arc and \(r_{j}\) the local curvature radius. Since we use a step of \(\Delta S=1\) pixel in the iterative tracing the resulting angle for each segment point is

$$ \alpha _{j}=\alpha _{j-1}+\frac{1}{r_{j}}. $$
(11)

Assuming a constant radius \(r_{j}=R\) along a segment of \(N_{L}\) points with length coordinates along the segment \(s(j)=j\) (in pixels, since \(\Delta S=1\) pixel), Equation 11 becomes

$$ \alpha _{j}=\alpha _{0}+\frac{j}{R}. $$
(12)

We denote with \(s\) the segment length coordinates \(s_{k}\) of each, \(k=0,1,\ldots,N_{i}-1\) segment of the \(i\)th image in order to simplify the analysis. The calculation of the mean angle of such a segment yields \(\overline{\alpha }=\frac{\sum _{j=1}^{N_{L}} \alpha _{j}}{N_{L}}= \alpha _{0}+\frac{N_{L}+1}{2R}\), leading to a standard deviation of \(N_{L}\) discrete angles:

$$ \sigma _{\alpha }=\sigma [\alpha (s)] = \sqrt{ \frac{\sum _{j=1}^{N_{L}} (\alpha _{j}-\overline{\alpha })^{2}}{N_{L}-1}}= \frac{1}{2R}\sqrt{\frac{N_{L}(N_{L}+1)}{3}} .$$
(13)

As the segment’s length \(L=N_{L}\Delta S \stackrel{\Delta S=1}{\Longrightarrow } N_{L}=L\), Equation 13 becomes

$$ \sigma _{\alpha } (R,L) =\frac{1}{2R}\sqrt{\frac{L(L+1)}{3}} .$$
(14)

Appendix C: Application of the Method to Synthetic Data

In order to further demonstrate/examine the effectiveness of the designed chromospheric swirl detection algorithm in other scientific fields, we applied it to a high-contrast image of synthetic vortex flows. It was obtained by the NASA/Goddard Space Flight Center Scientific Visualization Studio, from a movie representing Gulf Stream Sea Surface CurrentsFootnote 2 that are depicted by synthetic streamlines as single, grayscale image (snapshot) with dimensions 1024×576 pixels. Since no time evolution is involved, the algorithm is only applied up to the first-level clustering and the determination of SCs (see Figure 13). During the application of the algorithm only the first-level clustering parameters required proper modification. Naturally, since in this case \(T=1\), we set \(MinPts=2\) requiring at least two points within \(\epsilon =2r_{min}\) in order to form an SC. The minimum radius was determined simply by the radius of the smallest visually-resolved vortex in the synthetic image, hence, \(r_{min}\simeq 8\) pixels, which led to \(\epsilon =16\) (see Section 3.4.1 for definitions of the parameters). The acquired results are presented in Figure 14. The results are quite promising since the detected vortex-like structures that are labeled as “Swirl Candidates” represent more than 90% of all visually resolved vortices within the field of view. Visual inspection shows that the algorithm fails to detect (i) elongated vortex-like structures, (ii) faint vortices at the edges of the image, and (iii) vortices with extremely small radii. Point (i) suggests that the automated swirl detection algorithm would probably fail to provide all swirling structures of solar images in which large projections effects are important i.e. of images taken close to the solar limb.

Figure 14
figure14

The grayscale image obtained from the NASA/Goddard Space Flight Center Scientific Visualization Studio (https://svs.gsfc.nasa.gov/3913) with the synthetic streamlines representing sea surface currents (top panel) and the same image after the application of the swirl detection algorithm up to the first level clustering and the determination of the “SCs” (bottom panel). Each SC is represented by a different color and depicted by its traced segments and their corresponding centers of curvature. Apart from the circular and spiral shaped SCs, several other obscure clusters of, probably short-lived, curved structures are found, as well as, probably persistent, highly curved parts of elongated vortices.

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Dakanalis, I., Tsiropoula, G., Tziotziou, K. et al. Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics. Sol Phys 296, 17 (2021). https://doi.org/10.1007/s11207-020-01748-3

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Keywords

  • Chromosphere, quiet
  • Turbulence