Between GR and a black box: Recent progress toward a hyper-empirical source localization algorithm for twenty-first-century gravitational wave astronomy

Currently, parameter estimation schemes in gravitational wave astronomy fall into one of two wildly divergent categories: matched filtering algorithms which rely on resource-intensive numerical modeling of full (strong curvature) general relativity or deep learning neural networks which rely on black box model training. The situation is only slightly improved for source localization algorithms, in which the parameters of interest are only the source angles of the gravitational wave, but which must run extremely quickly in order to allow electromagnetic follow-up as close as possible to the time of merger. In this talk, I will argue that a third alternative is to relax the framework of full general relativity used in matched filtering algorithms without losing control (and understanding) of the algorithm as in deep learning models. After a brief overview of the current state of the art in source localization algorithms -- both matched filter and neural network -- I will fill in some of the details of the hyper-empirical strategy suggested above. By building on my previous work using a combination of empirical signal modeling, physical intuition, and fast and powerful numerical methods, I will outline a novel approach to source localization that offers the potential to run as quickly as neural networks while maintaining full control over built-in assumptions (and therefore likely failure modes).