Speaker
Description
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission.
However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and other stochastic sources. Therefore the development of techniques to identify sources of these types is of significant interest. We present a method of anomaly detection techniques based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy named Gravitational Wave Anomalous Knowledge (GWAK). While the semi-supervised nature of the problem comes with a cost in terms of accuracy as compared to supervised techniques, there is a qualitative advantage in generalizing experimental sensitivity beyond pre-computed signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing alternative signal priors that capture some of the salient features of gravitational-wave signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify compact binary coalescences, detector glitches and also a variety of unmodeled astrophysical sources.
