A Machine Learning Application in Multi-Messenger Astrophysics

Explore a 45-minute conference talk on machine learning applications in multi-messenger astrophysics, presented by Irene Di Palma from Sapienza University of Rome. Delve into the challenges of detecting gravitational waves from core-collapse supernova explosions and learn about a novel method using machine learning techniques. Discover how this approach was tested by injecting signals into real noise data from the Advanced LIGO-Virgo network during the O2 observation run. Gain insights into the improved efficiency of signal classification and detection, with the potential to identify events up to 14 kpc away. Understand the importance of connecting multiple messengers, including neutrinos and electromagnetic signals, in advancing our understanding of these cosmic phenomena.

Irene Di Palma - A Machine Learning Application in Multi-Messenger Astrophysics - IPAM at UCLA