Combined geometrical modelling and white-light mass determination of coronal mass ejections

Abstract

Context. We use forward modelling on multi-viewpoint coronagraph observations to estimate the 3-dimensional morphology, initial speed and deprojected masses of Coronal Mass Ejections (CMEs). The CME structure is described via the Graduated Cylindrical Shell (GCS) model, which enables the measurement of CME parameters in a consistent and comparable manner. Aims. This is the first large-scale use of the GCS model to estimate CME masses, so we discuss inherent peculiarities and implications for the mass determination with a special focus on CME events emerging from close to the observer’s central meridian. Further, we analyse the CME characteristics best suited to estimate the CME mass in a timely manner to make it available to CME arrival predictions. Methods. We apply the method to a set of 122 bright events observed simultaneously from two vantage points with the COR2 coronagraphs onboard of the twin NASA STEREO spacecraft. The events occurred between January 2007 and December 2013 and are compiled in an online catalogue within the EU FP7 project HELCATS. We statistically analyse the derived CME parameters, their mutual connection and their relation to the solar cycle. Results. We show that the derived morphology of intense disk events is still systematically overestimated by up to a factor of 2 with stereoscopic modelling, which is the same order of magnitude as for observations from only one vantage point. The overestimation is very likely a combination of projection effects as well as the increased complexity of separating CME shocks and streamers from CME fronts for such events. We further show that CME mass determination of disk events can lead to overestimation of the mass by about a factor of 10 or more, in case of overlapping bright structures. Conclusions. We conclude that for stereoscopic measurements of disk events, the measurement of the initial CME speed is the most reliable one. We further suggest that our presented CME speed-mass correlation is most suited to estimate the CME mass early from coronagraph observations.

Three-dimensional Reconstruction of Coronal Mass Ejections by the Correlation-aided Reconstruction Technique through Different Stereoscopic Angles of the Solar Terrestrial Relations Observatory Twin Spacecraft

“…We use the CMEs listed in the Heliospheric Imager CME Join Catalogue (HIJoinCAT), as well as their kinematic properties in the Heliospheric Imager Geometrical Catalogue (HIGeoCAT, Barnes et al 2019), to achieve our goal in this study. These two catalogues are both generated by the Heliospheric Cataloguing, Analysis and Techniques (HELCATS, Barnes, et al 2019;Harrison et al 2018;Murray et al 2018;Plotnikov et al 2016;Pluta et al 2019) project funded under the European Union's Seventh Framework Programme for Research and Technological Development. HIJoinCAT contains the manually observational CME events simultaneously detected by two HI cameras since 2007, and HIGeoCAT provides their kinematic properties, including the propagation directions and speeds, estimated by using the Fixed-Phi, Harmonic Mean and Self-Similar Expansion techniques.…”

Evaluation of CME Arrival Prediction Using Ensemble Modeling Based on Heliospheric Imaging Observations

“…Most prediction models rely on images from coronagraphs that observe the solar corona out to a maximum plane-of-sky distance of 30 R (e.g. Dumbović et al, 2018;Kay, Mays, & Verbeke, 2020;Pluta, Mrotzek, Vourlidas, Bothmer, & Savani, 2019;Singh, Yalim, & Pogorelov, 2018). The big advantages of these observations are their availability in real-time and their relatively simple interpretation.…”

“…One of the most difficult CME properties to predict is the orientation of the magnetic field inside the CME, which is, at the same time, the most critical parameter due to the fact that a large southward magnetic field component facilitates the strongest geomagnetic storms. A large number of studies are currently tackling this task by developing new models that try to predict the orientation of the magnetic field at 1 AU (e.g., Kay et al, 2017;Kubicka et al, 2016;Möstl et al, 2018;Palmerio et al, 2017;Savani et al, 2015;Shiota & Kataoka, 2016;Singh et al, 2020;Verbeke et al, 2019).…”

Kinematics of coronal mass ejections in the LASCO field of view

“…It is worth mentioning that there are other CME catalogs mostly based on automatic measurements: Computer Aided CME Tracking (CACTus; Robbrecht & Berghmans 2004); Solar Eruptive Event 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); Multi-View CME (MVC; Vourlidas et al 2017); and the FP7 Heliospheric Cataloguing, Analysis and Technique Service (HELCATS; Harrison et al 2016), which uses manual inspection of STEREO/HI data. In particular, the Coronal Mass Ejection Kinematic Database Catalogue (KinCat; Pluta et al 2019), as part of the HELCATS catalog, has shown a similar study on a dataset of 122 CMEs to determine their initial speeds using GCS model. For our research, we decided to use the SOHO/LASCO catalog because the authors of this work are also contributors to this catalog.…”