An Application of Hierarchical Gaussian Processes to the Detection of Anomalies in Star Light Curves

Niall Twomey*, Haoyan Chen, Tom Diethe, Peter Flach

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

1 Citation (Scopus)
68 Downloads (Pure)

Abstract

This study is concerned with astronomical time-series called light-curves that represent the brightness of celestial objects over a period of time. We consider the task of finding anomalous 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 compared to conventional Gaussian Processes and incur negligible additional computational complexity. Moreover, since the additional parameters are objectively optimised in a principled probabilistic framework one does not need to resort to grid searches for parameter selection. Experimentally, we demonstrate that our approach outperforms several baselines on both synthetic and light-curve data. Of particular interest is that the proposed method generalises very well from small subsets of the data, achieving near perfect precision of outlier detection even with as few as seven instances.

Original languageEnglish
Pages (from-to)152-163
Number of pages12
JournalNeurocomputing
Volume342
Early online date4 Feb 2019
DOIs
Publication statusPublished - 21 May 2019

Keywords

  • Astronomical data
  • anomaly detection
  • gaussian processes

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