Continuous gravitational waves from neutron stars have long been an illusive target for ground based gravitational waves detectors. Their discovery would allow us to probe further into the structure of a neutron star, aiding the efforts of identifying an equation of state. There are many challenges in searching for these signals, including exploring large parameter spaces and analysing high volumes of data. In this talk I will describe some of these challenges and some of the solutions currently being used. I will focus on SOAP, which is a rapid search for long duration gravitational wave signals which uses multiple neural network models to identify signals and estimate Bayesian posterior distributions on some neutron star parameters
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