An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492003" target="_blank" >RIV/00216208:11320/24:10492003 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=r5MrnHcMam" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=r5MrnHcMam</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3847/1538-4365/ad2dea" target="_blank" >10.3847/1538-4365/ad2dea</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning

Popis výsledku v původním jazyce

Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME&apos;s kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu&apos;s method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME&apos;s motion in time-series images and finally determine the CME&apos;s kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs&apos; geoeffectiveness.

Název v anglickém jazyce

An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning

Popis výsledku anglicky

Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME&apos;s kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu&apos;s method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME&apos;s motion in time-series images and finally determine the CME&apos;s kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs&apos; geoeffectiveness.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10305 - Fluids and plasma physics (including surface physics)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Astrophysical Journal, Supplement Series

ISSN

0067-0049

e-ISSN

1538-4365

Svazek periodika

271

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

59

Kód UT WoS článku

001200083900001

EID výsledku v databázi Scopus

2-s2.0-85190166194