collaborators

9 papers

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

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…