Speaker
Description
We investigate data-driven strategies for identifying and predicting geoeffective events using long-term space environment observations. The study explores different unsupervised learning approaches for detecting statistical anomalies in solar wind in-situ measurements and geomagnetic data, with the aim of enhancing our understanding of solar-terrestrial interaction. Such anomalies may correspond to precursors or signatures of geomagnetic disturbances [1], particularly when the Natural Time Analysis [2] is applied to ground-based geomagnetic indices, such as SYM-H, to refine storm onset definitions beyond conventional threshold-based approaches.
[1] Sabrina Guastavino et al, Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches, 2024 ApJ 971 94.
[2] Panayiotis A. Varotsos et al. Complexity measure in natural
time analysis identifying the accumulation of stresses before major earthquakes, Scientific Reports 14.1 (2024): 30828.
| Do you plan to attend in-person or online? | In-person |
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