Speaker: Alvin Chua (JPL)
Title: From modelling to inference: Order reduction and deep learning in gravitational-wave astronomy
Abstract: Our ability to do science with present and future gravitational-wave observatories is contingent on (i) the construction of waveform models that describe the signals from astrophysical sources, and (ii) the application of Bayesian methods to infer the parameters of any detected source. The accuracy requirements in both of these procedures come with a hefty computational cost. In this talk, I discuss the growing use of strategies such as reduced-order modelling and deep learning to address this problem. I also introduce a new universal framework that connects modelling and inference in a computationally efficient way, without sacrificing accuracy.