Period analysis of variable stars by robust smoothing |
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Authors: | Hee-Seok Oh Doug Nychka Tim Brown Paul Charbonneau |
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Institution: | University of Alberta, Edmonton, Canada.; National Center for Atmospheric Research, Boulder, USA. |
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Abstract: | Summary. The objective is to estimate the period and the light curve (or periodic function) of a variable star. Previously, several methods have been proposed to estimate the period of a variable star, but they are inaccurate especially when a data set contains outliers. We use a smoothing spline regression to estimate the light curve given a period and then find the period which minimizes the generalized cross-validation (GCV). The GCV method works well, matching an intensive visual examination of a few hundred stars, but the GCV score is still sensitive to outliers. Handling outliers in an automatic way is important when this method is applied in a 'data mining' context to a vary large star survey. Therefore, we suggest a robust method which minimizes a robust cross-validation criterion induced by a robust smoothing spline regression. Once the period has been determined, a nonparametric method is used to estimate the light curve. A real example and a simulation study suggest that the robust cross-validation and GCV methods are superior to existing methods. |
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Keywords: | Generalized cross-validation Period Periodic function Robust spline regression Smoothing spline regression |
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