Deep Learning Algorithm for Core-Collapse Supernova Detection
Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube
Course Description
Overview
Explore a 37-minute lecture on developing deep learning algorithms for detecting core-collapse supernovae through gravitational waves. Delve into the exciting field of multi-messenger astronomy, focusing on how gravitational waves and neutrinos provide unique insights into extreme cosmic events. Discover the potential of these signals to reveal crucial information about supernova core dynamics, explosion mechanisms, protoneutron star evolution, core rotation rates, and the nuclear equation of state. Learn about the development and application of machine learning techniques to enhance gravitational wave signal detection from core-collapse supernovae, including results obtained from real detector noise data.
Syllabus
Irene Di Palma - Deep learning algorithm for core-collapse supernova detection
Taught by
Institute for Pure & Applied Mathematics (IPAM)
Related Courses
Inflationary CosmologyMassachusetts Institute of Technology via World Science U Gravity! The Big Bang, Black Holes and Gravitational Waves
Paris Diderot University via FutureLearn Gravité! Du Big Bang aux Trous Noirs
Paris Diderot University via France Université Numerique Einstein's Astrophysical Messengers
Louisiana State University via World Science U Introduction into General Theory of Relativity
Higher School of Economics via Coursera