Rapidly Searching for Continuous Gravitational Waves

Explore the challenges and solutions in searching for continuous gravitational waves from neutron stars in this 31-minute conference talk by Joseph Bayley from the University of Glasgow. Delve into the complexities of analyzing large parameter spaces and high volumes of data to detect these elusive signals. Learn about SOAP, a rapid search method utilizing multiple neural network models to identify signals and estimate Bayesian posterior distributions on neutron star parameters. Gain insights into the potential impact of discovering continuous gravitational waves on understanding neutron star structure and equations of state. Examine traditional search methods, the role of IPAM, training data, efficiency curves, and more constraining distributions in the quest to detect these long-duration gravitational wave signals.

Overview
What are we searching for
How do we search
Traditional searches
What is IPAM
Inputs
Training data
Efficiency curve
More constraining distributions