Distinguishing between Flaring and Non-Flaring Active Regions: A Machine Learning Perspective

Abstract

Large scale solar eruptions significantly impact space weather and damages space-based human infrastructures. It is necessary to predict large scale solar eruptions, which will enable us to protect our vulnerable infrastructures of modern society. We aim to investigate the difference between flaring and non-flaring active regions. We use photospheric vector magnetogram data from Solar Dynamic Observatory's Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We build a database of flaring and non-flaring active region observed on the solar surface from the years 2010 to 2017. We train the machine learning algorithm by the time evolution of these active region parameters. Finally, we estimate the performance obtained from this machine learning algorithm. We find the strength of some magnetic parameters namely total unsigned magnetic flux, total unsigned magnetic helicity, total unsigned vertical current and total photospheric magnetic energy density in flaring active regions are much higher compared to the non-flaring ones. These magnetic parameters in the flaring active region are highly evolving and complex. We are able to obtain good forecasting capability with a relatively high value of true skill statistic (TSS). We also find that time evolution of total unsigned magnetic helicity and total unsigned magnetic flux have very high ability to distinguish flaring and non-flaring active regions. It is possible to distinguish flaring active region from the non-flaring one with good accuracy. We confirm that there is no single common parameter which can distinguish all flaring active regions from the non flaring one. However, time evolution of top few magnetic parameters namely total unsigned magnetic flux and total unsigned magnetic helicity have very high distinguishing capability.

13 Pages, 6 Figures

References in corpus (19)

• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Toward Reliable Benchmarking of Solar Flare Forecasting Methods
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Predicting Coronal Mass Ejections Using Machine Learning Methods
• What is the relationship between photospheric flow fields and solar flares?
• Coronal mass ejection initiation: On the nature of the Flux Cancellation Model
• The variation of relative magnetic helicity around major flares
• On the injection of helicity by shearing motion of fluxes in relation to Flares and CMEs
• Using Machine Learning Methods to Forecast If Solar Flares Will Be Associated with CMEs and SEPs
• The Occurrence and Speed of CMEs Related to Two Characteristic Evolution Patterns of Helicity Injection in Their Solar Source Regions
• Magnetic Flux Change and Cancellation Associated with X-class and M-class Flares
• Study of Magnetic Helicity Injection in the Active Regions NOAA 9236 Producing Multiple Flare-associated CME Events
• Resolving the Azimuthal Ambiguity in Vector Magnetogram Data with the Divergence-Free Condition: Application to Discrete Data
• Machine learning reveals systematic accumulation of electric current in lead-up to solar flares
• The Relationship between Solar Coronal X-Ray Brightness and Active Region Magnetic Fields: A Study Using High Resolution Hinode Observations
• Hemispheric Preference and Cyclic Variation of Solar Filament Chirality from 2000 to 2016
• Data Handling and Assimilation for Solar Event Prediction

Cited by in corpus (5)

• Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks
• Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
• Long-Term Evolution of Magnetic Fields in Flaring Active Region NOAA 12673
• ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
• A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators