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20202026
most citedPredicting the Emergence of Solar Active Regions Using Machine Learning

6 citations · 7 across the 13 of their papers we have counts for

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13 papers · 1 filter

astro-ph.SR2026

Supergranulation as a Tracer of Solar-Cycle Variability

Irina N. Kitiashvili, Andrew A. Ngo, Spiridon Kasapis

Supergranulation is one of the dominant scales of near-surface solar convection and provides an important diagnostic for studying the interaction between convective flows, rotation…

astro-ph.SR2026★ 1 cited

Review of Machine Learning Models for Solar Energetic Particle Prediction

Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman +73

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond…

astro-ph.SR2026

Prediction of Magnetic Flux Evolution During Solar Active Region Emergence using Long Short-Term Memory Networks

Eren Dogan, Spiridon Kasapis, Sarang Patil +5

Solar active regions (ARs) are the primary drivers of space weather events, making their early prediction crucial for operational forecasting systems. We develop machine learning m…

astro-ph.SR2026

Parker Solar Probe observations of solar energetic particle (SEP) events with inverse velocity arrival (IVA) features

Zigong Xu, C. M. S. Cohen, R. A. Leske +21

In SEP events, velocity dispersion (VD) is characterized by the earlier arrival of faster, higher-energy particles relative to slower ones, assuming negligible acceleration time an…

astro-ph.SR2026

SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions

Spiridon Kasapis, Eren Dogan, Irina N. Kitiashvili +6

The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore…

astro-ph.SR2026

Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers

Jonas Tirona, Sarang Patil, Spiridon Kasapis +5

Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting. Building on established Long Short-Term Memory (LSTM) based approaches…

astro-ph.SR2025

Surya: Foundation Model for Heliophysics

Sujit Roy, Johannes Schmude, Rohit Lal +30

Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observator…

astro-ph.SR2025

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction

Sujit Roy, Dinesha V. Hegde, Johannes Schmude +22

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine…

astro-ph.SR2025

Structure and Dynamics of the Sun's Interior Revealed by Helioseismic and Magnetic Imager

Alexander Kosovichev, Sarbani Basu, Yuto Bekki +43

High-resolution helioseismology observations with the Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO) provide a unique three-dimensional view of the…

astro-ph.SR2024

Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks

Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev +1

We developed Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Using the Doppler shift velocity, the continuum intensity,…

astro-ph.SR2024

Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning

Spiridon Kasapis, Irina N. Kitiashvili, Paul Kosovich +4

Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in mos…

astro-ph.SR2024★ 6 cited

Predicting the Emergence of Solar Active Regions Using Machine Learning

Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev +2

To create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic f…

astro-ph.SR2024

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-Weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Paul A. Kosovich, Alexander G. Kosovichev, Viacheslav M. Sadykov +8

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager onboard Solar and Heliospheric Observatory) Active Region Pa…