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Using conditional entropy to identify periodicity
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This paper presents a new period finding method based on conditional entropy that is both efficient and accurate. We demonstrate its applicability on simulated and real data. We find that it has comparable performance to other information-based techniques with simulated data but is superior with real data, both for finding periods and just identifying periodic behaviour. In particular, it is robust against common aliasing issues found with other period-finding algorithms.
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Cited by 1 Pith paper
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VarWISE: Infrared Variability via NEOWISE Single Exposure Photometry
VarWISE catalog identifies 457,080 high-confidence infrared variables (49.81% new) and an extended set of 1.9 million from NEOWISE photometry via spatial clustering, VARnet detection, and XGBoost classification.
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