Testing and Improving a Set of Morphological Predictors of Flaring Activity

Kontogiannis, Ioannis; Georgoulis, Manolis K.; Park, Sung-Hong; Guerra, Jordan A.

doi:10.1007/s11207-018-1317-2

Cited by 31 publications

(45 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Benvenuto et al (2018) use Fuzzy C-Means-an unsupervised machine learning method-in combination with some of the mentioned supervised methods for solar flare prediction. Approaches such as Guerra et al (2018) and Kontogiannis et al (2018), while not machine-learning approaches themselves, provide statistical tools for evaluating the engineered features in terms of their potential advantage in machine learning models. Finally, while most of the above methods focus on developing ML models using engineered features, recent methods have employed a convolutional neural network (CNN) approach which automatically extracts features from raw magnetogram data that are important to predicting flares (Huang et al, 2018;Park et al, 2018;Zheng et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Leveraging the mathematics of shape for solar magnetic eruption prediction

Deshmukh

Berger²,

Bradley

et al. 2020

J. Space Weather Space Clim.

View full text Add to dashboard Cite

Current operational forecasts of solar eruptions are made by human experts using a combination of qualitative shape-based classification systems and historical data about flaring frequencies. In the past decade, there has been a great deal of interest in crafting machine-learning (ML) flareprediction methods to extract underlying patterns from a training set-e.g., a set of solar magnetogram images, each characterized by features derived from the magnetic field and labeled as to whether it was an eruption precursor. These patterns, captured by various methods (neural nets, support vector machines, etc.), can then be used to classify new images. A major challenge with any ML method is the featurization of the data: pre-processing the raw images to extract higherlevel properties, such as characteristics of the magnetic field, that can streamline the training and use of these methods. It is key to choose features that are informative, from the standpoint of the task at hand. To date, the majority of ML-based solar eruption methods have used physics-based magnetic and electric field features such as the total unsigned magnetic flux, the gradients of the fields, the vertical current density, etc. In this paper, we extend the relevant feature set to include characteristics of the magnetic field that are based purely on the geometry and topology of 2D magnetogram images and show that this improves the prediction accuracy of a neural-net based flare-prediction method.

show abstract

Section: Introductionmentioning

confidence: 99%

Leveraging the mathematics of shape for solar magnetic eruption prediction

Deshmukh

Berger²,

Bradley

et al. 2020

J. Space Weather Space Clim.

View full text Add to dashboard Cite

show abstract

“…Solar flares of X1 class are identified by ICAO as requiring a moderate space weather advisory of likely weak HF radio communication, while an X10 flare requires a severe advisory due to likely HF radio blackout conditions. Forecasting of solar flare occurrence is an outstanding problem (Georgoulis, 2012;Kontogiannis et al, 2018). Predictive techniques using morphological methods based on observed parameters, such as photospheric magnetograms of solar active regions, have been developed but have low skill scores, particularly for large, infrequent flares (Barnes et al, 2016;Murray et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Developing a Nowcasting Capability for X‐Class Solar Flares Using VLF Radiowave Propagation Changes.

et al. 2019

View full text Add to dashboard Cite

A technique for analyzing very low frequency (VLF) radiowave signals is investigated in order to achieve rapid, real-time detection of large solar flares, through the monitoring of changes in VLF radio signal propagation conditions. The reliability of the use of VLF phase and amplitude perturbations to determine the X-ray fluxes involved during 10 large solar flare events (>X1) is examined. Linear regression analysis of signals from the NPM transmitter in Hawaii, received at Arrival Heights, Scott Base, Antarctica, over the years 2011-2015 shows that VLF phase perturbations during large solar flares have a 1.5-3 times lower mean square error when modeling the long wavelength X-ray fluxes than the equivalent short wavelength fluxes. The use of VLF amplitude observations to determine long or short wavelength X-ray flux levels have a 4-10 times higher mean square error than when using VLF phase. Normalized linear regression analysis identifies VLF phase as the most important parameter in the regression, followed by solar zenith angle at the midpoint of the propagation path, then the initial solar X-ray flux level (from 5 min before the impact of the solar flare), with F10.7 cm flux from the day beforehand providing the least important contribution. Transmitter phase measurements are more difficult to undertake than amplitude. However, networks of VLF receivers already exist which include the high quality phase capability required for such a nowcasting product. Such narrowband VLF data can be a redundant source of flare monitoring if satellite data is not available.

show abstract

“…These loop-like structures were sometimes seen to connect the two regions. Kontogiannis et al (2018) demonstrated that some of these low-lying loops were heated to transition region temperatures.…”

Section: Chromospheric Response and Subsequent Evolutionmentioning

confidence: 98%

“…The Fe xii slot images are co-aligned with the XRT images since they represent roughly the same coronal temperatures and structures. Further details on the observations, the speckle reconstruction procedure of the Hα observations, and some preliminary reduction steps are given in Kontogiannis et al (2010Kontogiannis et al ( , 2011Kontogiannis et al ( , 2018.…”

Section: Observations and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Emergence of small-scale magnetic flux in the quiet Sun

Kontogiannis

Tsiropoula²,

Tziotziou³

et al. 2020

A&A

Self Cite

View full text Add to dashboard Cite

Context. We study the evolution of a small-scale emerging flux region (EFR) in the quiet Sun, from its emergence in the photosphere to its appearance in the corona and its decay. Aims. We track processes and phenomena that take place across all atmospheric layers; we explore their interrelations and compare our findings with those from recent numerical modelling studies. Methods. We used imaging as well as spectral and spectropolarimetric observations from a suite of space-borne and ground-based instruments.Results. The EFR appears in the quiet Sun next to the chromospheric network and shows all morphological characteristics predicted by numerical simulations. The total magnetic flux of the region exhibits distinct evolutionary phases, namely an initial subtle increase, a fast increase with a cotemporal fast expansion of the region area, a more gradual increase, and a slow decay. During the initial stages, fine-scale G-band and Ca ii H bright points coalesce, forming clusters of positive-and negative-polarity in a largely bipolar configuration. During the fast expansion, flux tubes make their way to the chromosphere, pushing aside the ambient magnetic field and producing pressure-driven absorption fronts that are visible as blueshifted chromospheric features. The connectivity of the quiet-Sun network gradually changes and part of the existing network forms new connections with the newly emerged bipole. A few minutes after the bipole has reached its maximum magnetic flux, the bipole brightens in soft X-rays forming a coronal bright point. The coronal emission exhibits episodic brightenings on top of a long smooth increase. These coronal brightenings are also associated with surge-like chromospheric features visible in Hα, which can be attributed to reconnection with adjacent small-scale magnetic fields and the ambient quiet-Sun magnetic field. Conclusions. The emergence of magnetic flux even at the smallest scales can be the driver of a series of energetic phenomena visible at various atmospheric heights and temperature regimes. Multi-wavelength observations reveal a wealth of mechanisms which produce diverse observable effects during the different evolutionary stages of these small-scale structures.

show abstract

Testing and Improving a Set of Morphological Predictors of Flaring Activity

Cited by 31 publications

References 29 publications

Leveraging the mathematics of shape for solar magnetic eruption prediction

Leveraging the mathematics of shape for solar magnetic eruption prediction

Developing a Nowcasting Capability for X‐Class Solar Flares Using VLF Radiowave Propagation Changes.

Emergence of small-scale magnetic flux in the quiet Sun

Contact Info

Product

Resources

About