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

Research output: Contribution to journalArticle

Original languageEnglish
JournalNeurocomputing
DOIs
DateAccepted/In press - 26 Nov 2018
DatePublished (current) - 4 Feb 2019

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.

    Research areas

  • Astronomical data, anomaly detection, gaussian processes

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Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0925231219301328. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2 MB, PDF-document

    Embargo ends: 4/02/20

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