SITCoM: SiRGraF Integrated Tool for Coronal dynaMics

The white-light solar corona is associated with large-scale eruptions, coronal mass ejections (CMEs; Hundhausen et al., 1984; Gosling, 1993; Webb and Howard, 2012), reconnection-based plasmoids (e.g. Riley et al., 2007; Guo et al., 2013; Patel et al., 2020; Lee et al., 2020), coronal inflows (e.g. Wang et al., 1999; Sheeley and Wang, 2001, 2007, 2014; Hess and Wang, 2017), streamer waves (Chen et al., 2010a; Feng et al., 2013a; Decraemer et al., 2020a). All these phenomena are seen to exhibit a wide range of properties and are primarily observed and studied with white-light coronagraph imagery of Large Angle Spectrometric Coronagraph (LASCO; Brueckner et al., 1995) of Solar and Heliospheric Observatory (SOHO) and Sun Earth Connection Coronal and Heliospheric Imager (SECCHI; Howard et al., 2008) onboard Solar Terrestrial Relations Observatory (STEREO). It has been recently emphasized that most of the dynamics occur in the region from 1.5-6 R_⊙ defined as the middle corona (West et al., 2022). To capture such a wide variety of phenomena, it becomes important to separate these structures from the background and, at the same time, enhance their visibility in the coronagraph FOV of observation.

A couple of algorithms have been developed recently for white-light datasets to separate out the dynamic structures and reduce coronal intensity gradient. Normalizing radial gradient filter (NRGF; Morgan et al., 2006) and Simple Radial Gradient Filter (SiRGraF; Patel et al., 2022; Patel, 2022) are two such processing algorithms. Along similar lines, some algorithms have been used to enhance the off-limb structures in extreme ultraviolet images (Masson et al., 2014; Patel et al., 2021; Seaton et al., 2021, 2023). In the manual CME detection catalog of NASA Community Data Analysis Workshop (CDAW; Yashiro et al., 2004), CMEs can be tracked in their web interface by manually clicking the points in the running difference images. Automated CME detection has been performed with the aid of feature detection and image processing algorithms such as Computer Aided CME Tracking (CACTus; Robbrecht and Berghmans, 2004; Pant et al., 2016), Solar Eruptive Events Detection System (SEEDS; Olmedo et al., 2008), Automatic Recognition of Transient Events and Marseille Inventory from Synoptic maps (ARTEMIS; Boursier et al., 2009), Coronal Image Processing (CORIMP; Byrne et al., 2012; Morgan et al., 2012), CMEs Identification in Inner Solar Corona (CIISCO; Patel et al., 2021) and algorithms based on machine learning (Qu et al., 2006; Zhang et al., 2017). These algorithms have been mainly designed for and applied on large-scale transients, but not on small-scale flows and blobs-like structures.

Streamers are large-scale quasi-static bright structures and are seen to be stretched from the inner to outer corona in the eclipse images (Koutchmy and Livshits, 1992). CMEs are observed to displace streamers sideways and generate streamer oscillations (Chen et al., 2010b; Feng et al., 2013b; Kwon et al., 2013). The statistical study on streamer waves suggested that they can be a plausible candidate for coronal seismology (Decraemer et al., 2020b). In the context of automatic detection of transverse oscillations in the solar corona, Northumbria University Wave Tracking (NUWT; Morton et al., 2016) was developed and extended by (Weberg et al., 2018). The use of NUWT can be advantageous in carrying out statistical analysis of transverse waves. However, the NUWT code is best applicable for decayless waves observed in coronal loops (Weberg et al., 2018). Furthermore, the automatic identification and fitting of waves in time-distance ($x-t$) diagrams can be affected by the complex and varying backgrounds, as well as the presence of neighboring structures, which may necessitate manual fitting procedures. Additionally, there is a lack of a tool for quickly analyzing wave properties.

In recent times, Jhelioviewer (Müller et al., 2017), an open-source data visualization tool was developed with basic image processing and integration of different catalogs for quick analysis before proceeding to an in-depth one. An automated data-mining-based web interface combined with feature detection has been developed as a part of the Heliophysics Events Knowledgebase (HEK; Hurlburt et al., 2012). Using the multi-instrument observations and tracking CMEs, solar energetic particles (SEPs), co-rotating interaction regions (CIRs), etc a community-led effort by European Space Agency’s Modelling and Data Analysis Working Group (MADAWG) developed several tools, e.g. magnetic connectivity tool (Rouillard et al., 2020). Another web-based interface to track the CMEs, SEPs, and CIRs from the inner corona to the outer heliosphere is the ‘Propagation Tool’ (Rouillard et al., 2017). Solar MAgnetic Connection Haus (SolarMACH; Gieseler et al., 2023), another open-source tool was developed recently for the visualization of magnetic configurations and connections with various spacecraft and planetary bodies in the heliosphere. To track the CMEs based on multi-viewpoint observations from STEREO and SOHO coronagraphs, a web-based application called STEREO CME Analysis Tool (StereoCAT; Millward et al., 2013) is hosted at NASA Community Coordinated Modeling Center (https://ccmc.gsfc.nasa.gov/analysis/stereo/).

All these tools have been supported with basic image processing algorithms and are helpful in quick analysis serving their purposes. Often the output of these tools has also been part of in-depth scientific analysis. In this paper we introduce an open-source tool, SiRGraF Integrated Tool for COronal dynaMics (SITCoM), utilizing the SiRGraF algorithm and bringing out the dynamic features of the solar corona. This tool is currently targeted for studying the kinematics of CMEs, plasmoids, streamer blobs, outflows, inflows, etc., and the analysis of streamer waves and similar oscillatory phenomena. This paper is arranged as follows: in Section 4 we introduce and explain the architecture of the tool. The application of SITCoM for two broad aspects with illustrative examples is presented in Section 5 followed by a summary and discussions along with the scope of future improvements in Section 6.

sitcom is fully developed using Python and is temporarily made available as a PyPi package¹¹1PyPi: https://pypi.org/project/sitcom/ and is mainly compiled using PyQt and has other small dependencies. It can be installed using the command
pip install sitcom.
The software can be run for further use by the command in the terminal
python3 -m sitcom
Further updates will be released that will make it more flexible for different platforms. It is available on Github²²2GitHub:https://github.com/pu3/SITCoM as well for a better look at the code and to raise issues if any.

We acquired the level 0.5 data of the C2 coronagraph of LASCO from the Virtual Solar Observatory (VSO) for 29th August, 2022. We converted the data into Level 1 using the reduce_level_1.pro procedure available in the solar software (SSW) to correct for the image rotation and calibration. Other LASCO/C2 data presented in this paper were already level 1 corrected. Apart from these, STEREO/COR-1 data has also been used. Level 0.5 polarized brightness triplets (0^∘, 120^∘, 240^∘) of COR-1 are acquired from VSO for the date of 1st August, 2010. These images are reduced to level 1 using the secchi_prep.pro procedure of STEREO pipeline. The triplets are combined to obtain the total brightness images which are also calibrated and rotation corrected in this step. The level 1 data of LASCO/C2 and COR-1 are provided to the SITCoM analysis tool as FITS files.

We introduce a new tool, SITCoM, which is built upon the SiRGRaF algorithm for processing white-light coronagraph images. The application enables rapid analysis of coronal dynamics such as CMEs, plasma blobs, streamer waves, and similar transients, observed in white-light coronagraph data. The output of SITCoM can serve as preliminary analysis for in-depth scientific investigations or can be integrated into larger studies using various datasets. SITCoM is developed as an open-source application that features a simple user interface based on PyQT and other minor dependencies in Python. It can be installed using pip command in python.

The SITCoM graphical user interface (GUI) incorporates the SiRGRaF algorithm in its initial stage, which proves to be a simple yet effective tool for highlighting dynamic features in the corona. The dynamic features are enhanced against the background throughout the coronagraph FOV. We have illustrated the usage of SITCoM using LASCO and STEREO or white-light coronagraph data for various test cases. Since SiRGRaF has also been implemented on Solar Orbiter Metis data by Telloni et al. (2022), it can be used in the SITCoM as input along with the above mentioned data sets. Along with these data sets, the application of SiRGraF on the extreme ultraviolet (EUV) dataset will require detailed analysis and testing including image processing to enhance the structures observed in EUV passbands. The future updates of SITCoM will be equipped with such image-enhancing features to carry out the analysis presented here. Users have the capability to save the processed movie and individual frames in various formats. Moreover, SITCoM offers different intensity scaling, such as MinMax, Logarithmic, and Gamma, allowing users to enhance specific features based on their preferences and requirements.

To facilitate kinematics analysis of any sunward or anti-sunward moving structures in the data, SITCoM empowers users to generate height-time plots at different position angles. These plots provide valuable insights into the temporal evolution of the observed phenomena. The users can perform linear or higher-order fitting on these plots to derive essential parameters such as velocity or acceleration, depending on the type of fitting performed. Multiple measurements can be made at the same position angle or over desired angles to get an estimate of deviation in the measured kinematics throughout the large-scale structures.

Users can also investigate various types of oscillations and waves within a selected slit across a dynamic feature. By employing a decaying sine wave fitting technique, users can automatically or manually fit the resultant distance-time plot generated from the slit. The extracted parameters of the fitted wave can be saved for further analysis, providing insights into the oscillatory behavior of coronal features.

This GUI is not only a powerful tool for analyzing coronal dynamics but also saves time typically spent on plotting and fitting. By streamlining the analysis process, SITCoM provides useful results obtained with quick analysis. The combination of advanced algorithms and user-friendly tools facilitates a comprehensive understanding of the observed phenomena, enhancing our knowledge in this field. In the future, we plan to develop SITCoM for different platforms to make it more accessible to researchers worldwide. This will enable more scientists to benefit from its capabilities and contribute to our understanding of the Sun’s corona.

We plan to improve the SITCoM application with the addition of new features in the future. These include the implementation of fitting multiple ridges identified in the height-time plots for kinematics analysis. As a result, multiple transients at the same position angle can be tracked and characterized manually without having to generate the same x-t map repeatedly. Implementation of curved slit selection will enable a user to select a curved slit for the generation of x-t maps. Such a feature will be helpful to characterize the CME deflection, and non-radial flows (Alzate et al., 2023) as well as tracking the waves and oscillations at bigger regions of interest using a single large slit or a wide slit. A provision to save the coordinates corresponding to the slit location and the x-t map array in the form of FITS or ‘.sav’ file will be useful to revisit an analysis later or import to any other program when required.

These planned advancements will further enhance the capability of SITCoM and will be a part of its future releases.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

RP planned the development and overlooked the implementation of the tool. PU and AKS developed the tool in Python and carried out tests on different datasets. PU and AKS prepared the manuscript. All the authors took part in the discussion of the results.

A.K.S is supported by the Council of Scientific & Industrial Research (CSIR), India, under file no. 09/079(2872)/2021-EMR-I.

PU and AKS would like to thank ARIES for providing the computational facilities. The SECCHI data used here were produced by an international consortium of the Naval Research Laboratory (USA), Lockheed Martin Solar and Astrophysics Lab (USA), NASA Goddard Space Flight Center (USA), Rutherford Appleton Laboratory (UK), University of Birmingham (UK), Max-Planck-Institut for Solar System Research (Germany), Centre Spatiale de Li$\grave{e}$ge (Belgium), Institut d’Optique Th$\acute{e}$orique et Appliqu$\acute{e}$e (France), Institut d’Astrophysique Spatiale (France). We also thank NASA for making SOHO/LASCO data publicly available.

The STEREO/SECCHI and SOHO/LASCO data sets analyzed for this study can be found in their respective data archives under the open data policy. The data sets generated in this study can be made available upon request.