Detection of Solar Prominences using Automated Methods and Analysis of their Dynamic Pre-Eruptive Behaviour

Solar prominences, or filaments when viewed on the disk, are cool, dense plasma structures in the solar corona. Prominences are of great interest for space weather and can be seen as a natural laboratory to explore concepts of plasma physics and magnetism. The eruption of prominences can have a significant influence on the solar-terrestrial environment. However, accurately predicting these eruptions remains a challenge. We first explore the oscillatory dynamics of prominences in the Extreme Ultraviolet (EUV), and how these may change in the build-up to their eruption. Two events are studied. Firstly, an eruptive filament is analysed using data from the Solar and Heliospheric Observatory (SoHO), revealing similar ultra-long period oscillations in both the 304 Angstrom irradiance and the intensity timeseries from an image sequence in 195 Angstrom. For the second event, we develop and apply automated detection methods for EUV prominences observed by the twin spacecraft from the Solar Terrestrial Relations Observatory (STEREO) mission and the Solar Dynamics Observatory (SDO) near Earth. During March 2011, when each STEREO spacecraft is in quadrature with respect to the Earth, for two time ranges, we obtain longitudinal height profiles as a function of time. We also track the corresponding EUV filaments across the solar disk, which reveal the emergence of ultra-long-period oscillations in the EUV filament channels. Our analysis shows a correlation between the prominence's increasing height and the oscillation periods, suggesting a potential link to the subsequent eruption observed by the STEREO spacecraft off-limb. These findings offer new insights into prominence dynamics and may pave the way for improved eruption prediction. We also present new methods for detecting prominences, using both a multi-wavelength approach and a single-wavelength detection with machine learning. First, we analyse the distribution of prominences over a duration greater than one full solar cycle. We present a single-wavelength detection in 304 Angstrom, which uses a type of neural network known as a Mask Region-based Convolutional Neural Network (MRCNN). This reveals the latitudinal evolution of prominences across the solar cycle, while we find the prominence statistics lag behind the sunspot count by approximately 3 months. These results are consistent with observations and theory surrounding prominence formation. Several open questions remain regarding prominences, their dynamics and how we may optimise their detection. Discussions arising from this research may present opportunities for future projects and investigations into this field.<p></p>

Author dc:creator

• William Beckwith-Chandler (21041423)

• Prominence
• Oscillation
• Detection

Statement dc:rights

• All rights reserved
• Open Access after 2027-03-23

Identifier

10779/exe.31807522.v1

OAI identifier oai:identifier

oai:figshare.com:article/31807522