Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning

Korsós, M. B.; Erdélyi, R.; Liu, Jiajia; Morgan, Huw

doi:10.3389/fspas.2020.571186

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…For this purpose, we utilize random forest (RF; Breiman 2001), and extreme gradient boosting (XGBoost; Chen & Guestrin 2016) classifiers. These two models are popular tree-based learners that are extensively used in many areas of active research (Pal 2005;Fawagreh et al 2014;Sarica et al 2017;Tyralis et al 2019;Can et al 2021;Korsós et al 2021;Lavasa et al 2021;Osman et al 2021).…”

Section: Supervised Machine-learning Implementationmentioning

confidence: 99%

Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog

Rotti

Martens

2023

ApJS

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Solar energetic particle (SEP) events are one of the most crucial aspects of space weather. Their prediction depends on various factors including the source solar eruptions such as flares and coronal mass ejections (CMEs). The Geostationary Solar Energetic Particle (GSEP) events catalog was developed as an extensive data set toward this effort for solar cycles 22, 23, and 24. In the present work, we review and extend the GSEP data set by (1) adding “weak” SEP events that have proton enhancements from 0.5 to 10 pfu in the E >10 MeV channel and (2) improving the associated solar source eruptions information. We analyze and discuss spatiotemporal properties such as flare magnitudes, locations, rise times, and speeds and widths of CMEs. We check for the correlation of these parameters with peak proton fluxes and event fluences. Our study also focuses on understanding feature importance toward the optimal performance of machine-learning (ML) models for SEP event forecasting. We implement random forest, extreme gradient boosting, logistic regression, and support vector machine classifiers in a binary classification schema. Based on the evaluation of our best models, we find both the flare and CME parameters are requisites to predict the occurrence of an SEP event. This work is a foundation for our further efforts on SEP event forecasting using robust ML methods.

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Section: Supervised Machine-learning Implementationmentioning

confidence: 99%

Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog

Rotti

Martens

2023

ApJS

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“…There are many highly-developed SML classification methods, such as: Logistic Regression (Korsós et al 2021, LR) Support Vector Machines (Hearst 1998, SVM), artificial neural network (McCulloch & Pitts 1943, ANN), random forest (Tin Kam Ho 1995), etc. In this work, we choose the LR model over other methods like SVM, ANN and the likes for two main reasons.…”

Section: Selecting Sml Modelmentioning

confidence: 99%

Exploring TeV candidates of Fermi blazars through machine learning

Zhu¹,

Lin²,

Xiao³

et al. 2023

Preprint

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In this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a selection of optimal parameters, named features, and hyper-parameters whose values control the learning process and determine the values of features that a learning algorithm ends up learning, to discriminate TeV blazars from non-TeV blazars. In addition, it gives the probability (or logistic) that a source may be considered as a TeV blazar candidate. Non-TeV blazars with logistics greater than 80% are considered high-confidence TeV candidates. Using this technique, we identify 40 high-confidence TeV candidates from the 4FGL-DR2 / 4LAC-DR2 blazars and we build the feature hyper-plane to distinguish TeV and non-TeV blazars. We also calculate the hyper-planes for the 3FHL, 3HSP, and 2BIGB. Finally, we construct the broadband spectral energy distributions (SED) for the 40 candidates, testing for their detectability with various instruments. We find that 7 of them are likely to be detected by existing or upcoming IACT observatories, while 1 could be observed with EAS particle detector arrays.

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“…deep learning, and logistic regression ML. Topical Issue -CMEs, ICMEs, SEPs: Observational, Modelling, and Forecasting Advances (Huang et al, 2018;Camporeale, 2019;Korsós et al, 2021). ML methods offer the advantage of providing timely predictions once the models have been trained; see e.g.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach to the drag-based modelling of ICMEs

Chierichini,

Francisco,

Mugatwala

et al. 2024

J. Space Weather Space Clim.

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Coronal Mass Ejections (CMEs) are huge clouds of magnetised plasma expelled from the solar corona that can travel towards the Earth and cause significant space weather effects. The Drag-Based Model (DBM) describes the propagation of CMEs in an ambient solar wind as analogous to an aerodynamic drag. The drag-based approximation is popular because it is a simple analytical model that depends only on two parameters, the drag parameter $\gamma$ and the solar wind speed $w$. DBM thus allows us to obtain reliable estimates of CME transit time at low computational cost. Previous works proposed a probabilistic version of DBM, the Probabilistic Drag Based Model (P-DBM), which enables the evaluation of the uncertainties associated with the predictions. In this work, we infer the "a-posteriori" probability distribution functions (PDFs) of the $\gamma$ and $w$ parameters of the DBM by exploiting a well-established Bayesian inference technique: the Monte Carlo Markov Chains (MCMC) method. By utilizing this Bayesian method through two different approaches, an ensemble and an individual approach, we obtain specific DBM parameter PDFs for two ensembles of CMEs: those travelling with fast and slow solar wind, respectively. Subsequently, we assess the operational applicability of the model by forecasting the arrival time of CMEs. While the ensemble approach displays notable limitations, the individual approach yields promising results, demonstrating competitive performances compared to the current state-of-the-art, with a mean absolute error (MAE) of 9.86 ± 4.07 hours achieved in the best-case scenario.

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Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning

Cited by 7 publications

References 39 publications

Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog

Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog

Exploring TeV candidates of Fermi blazars through machine learning

A Bayesian approach to the drag-based modelling of ICMEs

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