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16 pages, 1464 KiB

Open AccessArticle

Classification of Major Solar Flares from Extremely Imbalanced Multivariate Time Series Data Using Minimally Random Convolutional Kernel Transform

by Kartik Saini, Khaznah Alshammari, Shah Muhammad Hamdi and Soukaina Filali Boubrahimi

Universe 2024, 10(6), 234; https://doi.org/10.3390/universe10060234 - 24 May 2024

Viewed by 364

Abstract

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar [...] Read more.

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence Learner (Mr-SEQL), and a Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over nine years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the True Skill Statistic (

T S S

) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MiniRocket, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics. Full article

(This article belongs to the Special Issue Solar and Stellar Activity: Exploring the Cosmic Nexus)

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20 pages, 3351 KiB

Open AccessArticle

Combining Empirical and Physics-Based Models for Solar Wind Prediction

by Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri and Shah Muhammad Hamdi

Universe 2024, 10(5), 191; https://doi.org/10.3390/universe10050191 - 24 Apr 2024

Cited by 1 | Viewed by 727

Abstract

Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 [...] Read more.

Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model’s performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models. Full article

(This article belongs to the Special Issue Solar and Stellar Activity: Exploring the Cosmic Nexus)

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Review

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19 pages, 6411 KiB

Open AccessReview

The Hearth of the World: The Sun before Astrophysics

by Gábor Kutrovátz

Universe 2024, 10(6), 256; https://doi.org/10.3390/universe10060256 - 7 Jun 2024

Viewed by 180

Abstract

This paper presents a historical overview of conceptions about the Sun in Western astronomical and cosmological traditions before the advent of spectroscopy and astrophysics. Rather than studying general cultural ideas, we focus on the concepts developed by astronomers or by natural philosophers impacting [...] Read more.

This paper presents a historical overview of conceptions about the Sun in Western astronomical and cosmological traditions before the advent of spectroscopy and astrophysics. Rather than studying general cultural ideas, we focus on the concepts developed by astronomers or by natural philosophers impacting astronomy. The ideas we investigate, from the works of Plato and Aristotle to William Herschel and his contemporaries, do not line up into a continuous and integrated narrative, since the nature of the Sun was not a genuine scientific topic before the nineteenth century. However, the question recurringly arose as embedded in cosmological and physical contexts. By outlining this heterogeneous story that spreads from transcendence to materiality, from metaphysics to physics, from divinity to solar inhabitants, we receive insight into some major themes and trends both in the general development of astronomical and cosmological thought and in the prehistory of modern solar science. Full article

(This article belongs to the Special Issue Solar and Stellar Activity: Exploring the Cosmic Nexus)

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