Prosiectau fesul blwyddyn
Crynodeb
Whilst the most dynamic solar active regions (ARs) are known to flare frequently, predicting the occurrence of individual flares and their magnitude, is very much a developing field with strong potentials for machine learning applications. The present work is based on a method which is developed to define numerical measures of the mixed states of ARs with opposite polarities. The method yields compelling evidence for the assumed connection between the level of mixed states of a given AR and the level of the solar eruptive probability of this AR by employing two morphological parameters: 1) the separation parameter (Formula presented.) and 2) the sum of the horizontal magnetic gradient (Formula presented.). In this work, we study the efficiency of (Formula presented.) and (Formula presented.) as flare predictors on a representative sample of ARs, based on the SOHO/MDIDebrecen Data (SDD) and the SDO/HMI  Debrecen Data (HMIDD) sunspot catalogues. In particular, we investigate about 1,000 ARs in order to test and validate the joint prediction capabilities of the two morphological parameters by applying the logistic regression machine learning method. Here, we confirm that the two parameters with their threshold values are, when applied together, good complementary predictors. Furthermore, the prediction probability of these predictor parameters is given at least 70% a day before.
Iaith wreiddiol  Saesneg 

Rhif yr erthygl  571186 
Nifer y tudalennau  8 
Cyfnodolyn  Frontiers in Astronomy and Space Sciences 
Cyfrol  7 
Dynodwyr Gwrthrych Digidol (DOIs)  
Statws  Cyhoeddwyd  18 Ion 2021 
Ôl bys
Gweld gwybodaeth am bynciau ymchwil 'Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.Prosiectau
 1 Wedi Gorffen

Solar System Physics at Aberystwyth University
Morgan, H., Cook, T., Gorman, M., Li, X., Pinter, B. & Taroyan, Y.
Science & Technology Facilities Council
01 Ebr 2019 → 31 Rhag 2022
Prosiect: Ymchwil a ariannwyd yn allanol