Supernova Affected Particle

Research on supernova-affected particles focuses on leveraging diverse data sources (photometry, spectroscopy, and simulations) to improve our understanding of supernovae and their impact on galactic environments. Current efforts utilize machine learning, particularly deep learning models like convolutional neural networks and novel architectures designed for multimodal data integration, to classify supernova types, predict their expansion, and efficiently simulate their effects on surrounding gas. These advancements enable more accurate cosmological measurements, improved galaxy formation simulations, and faster transient detection, significantly impacting our ability to study supernovae and their role in the universe.