Predicting Coronal Mass Ejections transit times to Earth with neural network

Abstract

Predicting transit times of Coronal Mass Ejections (CMEs) from their initial parameters is a very important subject, not only from the scientific perspective, but also because CMEs represent a hazard for human technology. We used a neural network to analyse transit times for 153 events with only two input parameters: initial velocity of the CME, $v$, and Central Meridian Distance, CMD, of its associated flare. We found that transit time dependence on $v$ is showing a typical drag-like pattern in the solar wind. The results show that the speed at which acceleration by drag changes to deceleration is $v\approx$500 km s${}^{-1}$. Transit times are also found to be shorter for CMEs associated with flares on the western hemisphere than those originating on the eastern side of the Sun. We attribute this difference to the eastward deflection of CMEs on their path to 1 AU. The average error of the NN prediction in comparison to observations is $\approx$12 hours which is comparable to other studies on the same subject.

References in corpus (7)

• Ensemble modeling of CMEs using the WSA-ENLIL+Cone model
• The Deflection of the Two Interacting Coronal Mass Ejections of 2010 May 23-24 as Revealed by Combined In situ Measurements and Heliospheric Imaging
• Relationship between Solar Energetic Particles and Properties of Flares and CMEs: Statistical Analysis of Solar Cycle 23 Events
• Connecting speeds, directions and arrival times of 22 coronal mass ejections from the Sun to 1 AU
• Kinematic Evolution of a Slow CME in Corona Viewed by STEREO-B on 8 October 2007
• Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project
• Combined Multipoint Remote and In Situ Observations of the Asymmetric Evolution of a Fast Solar Coronal Mass Ejection

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• Accurately constraining velocity information from spectral imaging observations using machine learning techniques
• Forbush Decrease Prediction Based on the Remote Solar Observations
• Predicting the Geoeffectiveness of CMEs Using Machine Learning
• Investigation of two coronal mass ejections from circular ribbon source region: Origin, Sun-Earth propagation and Geo-effectiveness