Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data
This provides a scalable framework for anomaly detection in gravitational wave data, which is incremental as it applies an existing method to a new domain.
The paper tackled the problem of detecting anomalies like gravitational wave signals in noisy data by proposing an unsupervised method using variational autoencoders, achieving an AUC of 0.89 on LIGO detector data.
Gravitational waves (GW), predicted by Einstein's General Theory of Relativity, provide a powerful probe of astrophysical phenomena and fundamental physics. In this work, we propose an unsupervised anomaly detection method using variational autoencoders (VAEs) to analyze GW time-series data. By training on noise-only data, the VAE accurately reconstructs noise inputs while failing to reconstruct anomalies, such as GW signals, which results in measurable spikes in the reconstruction error. The method was applied to data from the LIGO H1 and L1 detectors. Evaluation on testing datasets containing both noise and GW events demonstrated reliable detection, achieving an area under the ROC curve (AUC) of 0.89. This study introduces VAEs as a robust, unsupervised approach for identifying anomalies in GW data, which offers a scalable framework for detecting known and potentially new phenomena in physics.