Chinese Researchers Revolutionize Space Weather Forecasting

APP

Beijing: Chinese researchers have recently introduced a groundbreaking algorithm, featured in a research article in the Astrophysical Journal Supplement Series, designed to automatically extract kinematic parameters of coronal mass ejections (CMEs) through machine learning techniques.

This innovation underscores the algorithm’s profound implications in forecasting catastrophic space weather events.

CMEs represent massive eruptions of plasma ejected from the sun into interplanetary space, ranking as the most significant form of energy release within the solar system.

They stand as primary drivers of severe space weather phenomena, possessing the capacity to inflict substantial harm on both human activities and spacecraft operations in outer space.

As space activities and facilities continue to expand, the importance of detecting and tracking Coronal Mass Ejections (CMEs) is increasing, as highlighted in a recent study.

Understanding the dynamics of CMEs within the solar corona and interplanetary space is crucial for the field of space weather. Shen Fang, a researcher at the National Space Science Center of the Chinese Academy of Sciences, emphasized the significance of studying the positional relationships between CMEs and Earth’s orbit.

The study proposed a method consisting of three main steps: recognition, tracking, and determination of parameters. Initially, researchers trained a neural network to discern the presence of CMEs in images.

Subsequently, they identified and labeled regions with binary markers indicating CME presence. Finally, they tracked the motion of CMEs across time-series images and calculated kinematic parameters such as velocity, angular width, and central position angle.

Shen Fang noted that their algorithm is capable of detecting relatively weak CME signals and providing accurate morphology information about CMEs. This advancement is anticipated to greatly aid in issuing real-time CME warnings and predictions.