Speaker
Description
We present a study of deep convolutional autoencoders applied to anomaly detection of GW signals. This initial work focuses on short-duration signals (< 2s), corresponding to mergers that involve, or form, intermediate mass black holes. These burst-like signals are notably difficult to disentangle from both background noise and glitches that may occur during data taking. We utilise the simulated noise and merger catalogue provided as part of the Einstein Telescope Mock Data Challenge. Weak supervision is employed during training, whereby the model is directly optimised to separate 2D spectrograms containing IMBH merger signals (injected into ET noise) from those containing only noise.
The model shows excellent results in recovering the targeted IMBH merger signals, and a strong ability to generalise to masses below those seen during training. Current work focuses on the inclusion of glitches in training, with more complex network architecture being tested to provide 3-way noise, glitch and signal classification. Furthermore, the inclusion of networks purposed towards parameter estimation is under investigation, with the aim to thereby develop a classification and parameter estimation pipeline that is able to handle the high rate and diversity of signals we can expect in the Einstein Telescope era.