Multi Messenger Observation

Multi-messenger observation integrates data from diverse sources, such as gravitational waves, electromagnetic radiation, and even satellite imagery, to gain a more complete understanding of astrophysical events and Earth science phenomena. Current research focuses on improving data analysis techniques, including machine learning for real-time inference and novel compressed sensing algorithms for efficient data handling, as well as developing frameworks for aligning and integrating unpaired multimodal data. These advancements enable more accurate and timely scientific discoveries, enhancing our ability to study phenomena like binary neutron star mergers and improve Earth observation capabilities through agile satellite planning and coordinated multi-payload observations.