Recorded 01 December 2021. Irene Di Palma of the Sapienza University of Rome presents "Deep learning algorithm for core-collapse supernova detection" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics.
Abstract: The recent discovery of gravitational waves and high-energy cosmic neutrinos, marked the beginning of a new era of the multimessenger astronomy. These new messengers, along with electromagnetic radiation and cosmic rays, give new insights into the most extreme energetic cosmic events. Among them supernovae explosion is one of the challenging targets of this new astronomical approach.
Gravitational waves, much like neutrinos, are emitted from the innermost region of the core collapse supernova and thus convey information on the dynamics in the supernova core to the observer. They potentially carry information not only on the general degree of asymmetry in the dynamics of the core collapse supernova, but also more directly on the explosion mechanism, on the structural and compositional evolution of the protoneutron star, the rotation rate of the collapsed core, and the nuclear equation of state. The development of a new machine learning algorithm will be described to further improve the detectability of a gravitational wave signal from core collapse supernova and the results obtained in real detector noise data will be discussed.
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