The detection of gravitational waves (GW) from stellar binaries such as black hole and neutron star mergers have ushered in a new era of analyzing the universe. With this, the Laser Interferometer Gravitational-wave Observatory (LIGO) can peer into deep space giving astronomers the ability to uncover hidden stellar processes. Instrumental on the software side of these observations are the algorithms which pick up the faint signals of GWs from a strongly isolated and increasingly quantum noise environment. The identification of GWs presents itself as a good candidate for machine learning approaches which can learn complex non-linear relationships in their data.
The aim of this project is an exploration into the unsupervised regime of detection algorithms such as deep autoencoders for Gravitational Wave Anomaly Detection. Moreover, we propose a set of artificial neural network architectures for supervised learning in order to classify GWs on the labeled dataset. Eventually, we discuss the accuracy of both approaches and accelerate their inference by low-level optimization of code in hls4ml library and Intel oneAPI toolkits designed for cross-hardware deployment. Finally, we propose an experimental path for anomaly detection with biologically-inspired Spiking Neural Networks deployed on Intel Loihi neuromorphic chips and benefit from time-dependency of generated data