Anomaly Detection in Star Light Curves using Hierarchical Gaussian Processes

Hayoan Chen, Tom Diethe, Niall Twomey, Peter Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

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.
Original languageEnglish
Title of host publicationEuropean Symposium on Artificial Neural Networks
Subtitle of host publicationESANN
Place of PublicationBruges
PublisherEuropean Symposium on Artificial Neural Networks
Publication statusPublished - 27 Apr 2018

Structured keywords

  • Digital Health
  • SPHERE

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