Anomaly Detection in Star Light Curves using Hierarchical Gaussian Processes
Chen, Hayoan, Tom Diethe, Niall Twomey, Peter A. Flach · Bristol Research (University of Bristol) · 2018
Here we examine astronomical time-series called light-curve data, which represent the brightness of celestial objects over a period of time. We focus specifically on the task of finding anomalies in three sets of light-curves of periodic variable stars. We employ a hierarchical Gaussian process to create a general and stable model of time series for anomaly detection, and apply this approach to the light curve problem. Hierarchical Gaussian processes require only a few additional parameters than Gaussian processes and incur negligible additional computational complexity. Additionally, the additional parameters are objectively optimised in a principled probabilistic framework. Experimentally, our approach outperforms several baselines and highlights several anomalous light curves in the datasets investigated.