Variable Star Period Determination for Datasets with Sparse Time Sampling

Details Variable Star Period Determination for Datasets with Sparse Time Sampling Variable Star Period Determination for Datasets with Sparse Time Sampling

Jan 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21541808t&link_type=abstract

American Astronomical Society, AAS Meeting #215, #418.08; Bulletin of the American Astronomical Society, Vol. 42, p.275

Statistics

Computation

Scientific paper

We address the problem of period determination for poorly sampled light curves of pulsating variable stars, especially RR Lyrae type variables. We test the quality of several methods of period determination by attempting the recovery of periods from simulated datasets generated from light-curve templates. In particular, we seek to probe the data-poor limit beyond which we cannot reliably recover periods with each method. This is particularly relevant for space-based studies which typically have less time available for observing but can characterize RR Lyraes out to greater distances than ground-based studies. Our simulations cover a wide range of relevant parameter space, testing the efficacy of several methods at a variety of periods, sampling windows, light-curve amplitudes, filter combinations, and measurement errors. The primary methods examined include Lomb-Scargle periodograms, the Lafler-Kinman method, and light-curve-template fitting. The disparity in computation time required for these different methods further motivates the quantification of their success rates; to this end, we also consider the combination of the less computationally intensive Lomb-Scargle periodograms with the more robust template fitting in order to maximize efficiency.

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