Period estimation for sparsely-sampled quasi-periodic light curves applied to Miras
Autor: | Wenlong Yuan, Shiyuan He, Jianhua Z. Huang, James P. Long, Lucas M. Macri |
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Jazyk: | angličtina |
Rok vydání: | 2016 |
Předmět: |
FOS: Computer and information sciences
FOS: Physical sciences Parameter space 01 natural sciences Statistics - Applications 010104 statistics & probability symbols.namesake 0103 physical sciences Prior probability Applications (stat.AP) 0101 mathematics Instrumentation and Methods for Astrophysics (astro-ph.IM) 010303 astronomy & astrophysics Gaussian process Solar and Stellar Astrophysics (astro-ph.SR) Physics Basis (linear algebra) Sampling (statistics) Astronomy and Astrophysics Light curve Nonlinear system Astrophysics - Solar and Stellar Astrophysics Space and Planetary Science symbols Astrophysics - Instrumentation and Methods for Astrophysics Algorithm Test data |
Popis: | We develop a non-linear semi-parametric Gaussian process model to estimate periods of Miras with sparsely-sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequency parameter over a dense grid. A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting Period-Luminosity relations. Changes in v3: minor edits to match the published version. Software package and test data set available at http://github.com/shiyuanhe/varStar |
Databáze: | OpenAIRE |
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