| 1. |
- Sinha, Suvadip, et al.
(författare)
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A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting : Identifying High-performing Active Region Flare Indicators
- 2022
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 935:1
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Tidskriftsartikel (refereegranskat)abstract
- Solar flares create adverse space weather impacting space- and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains elusive. In the most comprehensive study to date, we conduct a comparative analysis of four popular ML techniques (k nearest neighbors, logistic regression, random forest classifier, and support vector machine) by training these on magnetic parameters obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for the entirety of solar cycle 24. We demonstrate that the logistic regression and support vector machine algorithms perform extremely well in forecasting active region flaring potential. The logistic regression algorithm returns the highest true skill score of 0.967 ± 0.018, possibly the highest classification performance achieved with any strictly parametric study. From a comparative assessment, we establish that magnetic properties like total current helicity, total vertical current density, total unsigned flux, R_VALUE, and total absolute twist are the top-performing flare indicators. We also introduce and analyze two new performance metrics, namely, severe and clear space weather indicators. Our analysis constrains the most successful ML algorithms and identifies physical parameters that contribute most to active region flare productivity.
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| 2. |
- Cooray, Vernon, 1952-, et al.
(författare)
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Comment on "Straight lightning as a signature of macroscopic dark matter"
- 2022
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Ingår i: Physical Review D. - : American Physical Society. - 2470-0010 .- 2470-0029. ; 105:8
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- In the discussed paper [N. Starkman, H. Winch, J. S. Sidhu, and G. Starkman, Straight lightning as a signature of macroscopic dark matter, Phys. Rev. D 103, 063024 (2021)], the authors have made several assumptions and statements concerning the initiation and propagation of lightning flashes induced by macroscopic dark matter passing through the atmosphere. The authors suggest that the path of dark matter can be identified by looking for lightning with straight channels, although such channels have not been previously reported. Even though we agree with the suggestion of the authors that macroscopic dark matter could, in theory, give rise to straight lightning channels, there are several statements in the paper that are not sufficiently clear and which could lead to misinterpretation. Our comments on the paper are the following.
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| 3. |
- Fontcuberta, Aleix Espuna, et al.
(författare)
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Forecasting Solar Cycle 25 with Physical Model-Validated Recurrent Neural Networks
- 2023
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Ingår i: Solar Physics. - : Springer Nature. - 0038-0938 .- 1573-093X. ; 298:1
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Tidskriftsartikel (refereegranskat)abstract
- The Sun's activity, which is associated with the solar magnetic cycle, creates a dynamic environment in space known as space weather. Severe space weather can disrupt space-based and Earth-based technologies. Slow decadal-scale variations on solar-cycle timescales are important for radiative forcing of the Earth's atmosphere and impact satellite lifetimes and atmospheric dynamics. Predicting the solar magnetic cycle is therefore of critical importance for humanity. In this context, a novel development is the application of machine-learning algorithms for solar-cycle forecasting. Diverse approaches have been developed for this purpose; however, with no consensus across different techniques and physics-based approaches. Here, we first explore the performance of four different machine-learning algorithms - all of them belonging to a class called Recurrent Neural Networks (RNNs) - in predicting simulated sunspot cycles based on a widely studied, stochastically forced, nonlinear time-delay solar dynamo model. We conclude that the algorithm Echo State Network (ESN) performs the best, but predictability is limited to only one future sunspot cycle, in agreement with recent physical insights. Subsequently, we train the ESN algorithm and a modified version of it (MESN) with solar-cycle observations to forecast Cycles 22 - 25. We obtain accurate hindcasts for Solar Cycles 22 - 24. For Solar Cycle 25 the ESN algorithm forecasts a peak amplitude of 131 +/- 14 sunspots around July 2024 and indicates a cycle length of approximately 10 years. The MESN forecasts a peak of 137 +/- 2 sunspots around April 2024, with the same cycle length. Qualitatively, both forecasts indicate that Cycle 25 will be slightly stronger than Cycle 24 but weaker than Cycle 23. Our novel approach bridges physical model-based forecasts with machine-learning-based approaches, achieving consistency across these diverse techniques.
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