Gravitational wave isolation with autoencoder neural network cascade

Detectors at the Laser Interferometer Gravitational Wave Observatory (LIGO), along with its sister detectors Virgo and Kamioka Gravitational Wave Detector (KAGRA), endlessly collect data to be analyzed in search of gravitational wave signals (GW signals) produced primarily by massive merger events involving colliding black holes and neutron stars. These detectors are the foundation upon which the new field of gravitational wave astronomy is built. However, the algorithms used to sift through this incoming data are extremely computationally expensive. They constantly run the risk of lagging behind the detectors, which would be catastrophic for astronomers searching for merger events, and their inherent rigidity becomes an obstruction to the expansion of these detectors’ range and sensitivity. To simplify and expedite the process of detecting and isolating GW signals, this paper presents a neural network (NN) cascade to automatically isolate GW signals in raw LIGO data. Perfecting such a NN system would improve the speed and efficiency of detecting and analyzing GW signals. This study uses a 2-stage cascade of convolutional autoencoders (CAEs) to detect and reconstruct GW signals buried in LIGO data, finding that it is effective at accurately isolating GW signals with very low latency on just one graphics processing unit (GPU). Ultimately, it is found that it is practical and likely beneficial for astronomers to use such a cascade to process incoming LIGO data.