Authors - Shraddha Mankar, Tanishq Thuse, Prasanna Khebade, Ritik Kumar Singh, Shravani Shirpurkar Abstract - Coronal Mass Ejections (CMEs) occurring in halo configuration are considered one of the most serious threats coming from space weather that can cause disruptions to most of the Earth’s geomagnetic facilities. The present study is about a hybrid machine learning system that detects the halo CMEs and predicts their Earth impact in real-time using the particle data coming from the in-situ India’s Aditya-L1 mission placed at L1 Lagrange point. We apply physics-informed feature extraction from SWIS-ASPEX payload measurements, obtaining alpha-to-proton density ratios, bulk velocity gradients, thermal parameters, and velocity anisotropy indices as CME markings. A Long Short-Term Memory (LSTM) neural network tuned through the Spider Cuckoo Optimization Algorithm processes 24-hour sequential windows of these features to distinguish between CME and non-CME events. The system also includes the modeling of Parker spiral propagation for Earth arrival time estimation and it is made available through a React-based dashboard with explainable AI components. The performance of the system reveals that it achieves a 98% detection rate along with a mean absolute error of 0.001 in the prediction of the normalized impact index. A comparison with historic halo CME catalogs indicates that our method has reduced false alarms by 85% when compared with threshold-based techniques while keeping the recall rate at 90%. The operational version of the system grants a 45-60 minute notification for the arrival of the CME, thus enabling the sensitive infrastructure to take preventive measures.
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