HaloSAT: AI-Powered CME & Space Weather Monitor

Problem: Coronal Mass Ejections (CMEs) are powerful solar eruptions that can trigger geomagnetic storms and disrupt satellites, communication systems, navigation, aviation, and electrical infrastructure. Timely detection and risk assessment are therefore critical for space-weather preparedness. Solution: HaloSAT is an end-to-end space weather monitoring platform designed to detect, analyze, predict, and visualize CME activity. The system combines real-time solar observations, multi-parameter CME detection, machine learning, satellite-related analysis, and interactive 3D visualization. Its workflow covers five major phases: real-time data acquisition, CME detection and classification, ML-based prediction, satellite/space-weather risk analysis, and interactive visualization. A dedicated field-data prediction component uses historical and incoming measurements to provide predictive insights. Target Users: Space agencies, satellite operators, aviation and communication organizations, power-grid operators, researchers, and academic institutions. Interaction: Users can monitor incoming solar data, review detected CME events, examine confidence scores and predictions, explore solar activity through interactive visualizations, and assess potential infrastructure risks. Creativity: HaloSAT brings real-time monitoring, AI/ML-based analysis, multi-parameter detection, 3D visualization, and satellite-level risk assessment together in a single platform developed by a two-student team. Efficiency: The LSTM model achieved 97.3% accuracy, while the monitoring pipeline refreshes data every 60 seconds and provides confidence-scored CME detection to support faster interpretation of solar events.