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How Many Twists Do Solar Coronal Jets Release?  

Jiajia Liu   Submitted: 2019-05-24 07:46

Highly twisted magnetic flux ropes, with finite length, are subject to kink instabilities, and could lead to a number of eruptive phenomena in the solar atmosphere, including flares, coronal mass ejections (CMEs) and coronal jets. The kink instability threshold, which is the maximum twist a kink-stable magnetic flux rope could contain, has been widely studied in analytical models and numerical simulations, but still needs to be examined by observations. In this article, we will study twists released by 30 off-limb rotational solar coronal jets, and compare the observational findings with theoretical kink instability thresholds. We have found that: 1) the number of events with more twist release becomes less; 2) each of the studied jets has released a twist number of at least 1.3 turns (a twist angle of 2.6π); and 3) the size of a jet is highly related to its twist pitch instead of twist number. Our results suggest that the kink instability threshold in the solar atmosphere should not be a constant. The found lower limit of twist number of 1.3 turns should be merely a necessary but not a sufficient condition for a finite solar magnetic flux rope to become kink unstable.

Authors: Jiajia Liu, Yuming Wang, Robertus Erdélyi
Projects: SDO-AIA

Publication Status: Accepted for publication in Frontiers in Astronomy and Space Sciences
Last Modified: 2019-05-24 10:19
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A New Tool for CME Arrival Time Prediction Using Machine Learning Algorithms: CAT-PUMA  

Jiajia Liu   Submitted: 2018-03-15 05:48

Coronal Mass Ejections (CMEs) are arguably the most violent eruptions in the Solar System. CMEs can cause severe disturbances in the interplanetary space and even affect human activities in many respects, causing damages to infrastructure and losses of revenue. Fast and accurate prediction of CME arrival time is then vital to minimize the disruption CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full-halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full-halo CMEs and using algorithms of the Support Vector Machine (SVM). We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions after applying CAT-PUMA to a test set, that is unknown to the engine, show a mean absolute prediction error ∼5.9 hours of the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hours. Comparison with other models reveals that CAT-PUMA has a more accurate prediction for 77% of the events investigated; and can be carried out very fast, i.e. within minutes after providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

Authors: Jiajia Liu, Yudong Ye, Chenlong Shen, Yuming Wang, Robert Erdélyi
Projects: SoHO-LASCO

Publication Status: Published, The Astrophysical Journal, 855:109 (10pp), 2018
Last Modified: 2018-03-16 13:38
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How Many Twists Do Solar Coronal Jets Release?
A New Tool for CME Arrival Time Prediction Using Machine Learning Algorithms: CAT-PUMA

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