Physics Journal
Articles Information
Physics Journal, Vol.2, No.2, Mar. 2016, Pub. Date: Jan. 21, 2016
Phemenological Modelling of a Group of Eclipsing Binary Stars
Pages: 140-150 Views: 1018 Downloads: 932
[01] Ivan L. Andronov, Department “High and Applied Mathematics”, Odessa National Maritime University, Odessa, Ukraine.
[02] Mariia G. Tkachenko, Department “High and Applied Mathematics”, Odessa National Maritime University, Odessa, Ukraine.
[03] Lidia L. Chinarova, Astronomical Observatory, Odessa National University, Odessa, Ukraine.
Phenomenological modeling of variable stars allows determination of a set of the parameters, which are needed for classification in the “General Catalogue of Variable Stars” and similar catalogs. We apply a recent method NAV (“New Algol Variable”) to eclipsing binary stars of different types. Although all periodic functions may be represented as Fourier series with an infinite number of coefficients, this is impossible for a finite number of the observations. Thus one may use a restricted Fourier series, i.e. a trigonometric polynomial (TP) of order s either for fitting the light curve, or to make a periodogram analysis. However, the number of parameters needed drastically increases with decreasing width of minimum. In the NAV algorithm, the special shape of minimum is used, so the number of parameters is limited to 10 (if the period and initial epoch are fixed) or 12 (not fixed). We illustrate the NAV method by application to a recently discovered Algol-type eclipsing variable 2MASS J11080308-6145589 (in the field of previously known variable star RS Car) and compare results to that obtained using the TP fits. For this system, the statistically optimal number of parameters is 44, but the fit is still worse than that of the NAV fit. Application to the system GSC 3692-00624 argues that the NAV fit is better than the TP one even for the case of EW-type stars with much wider eclipses. Model parameters are listed.
Astronomy, Stellar Astrophysics, Variable Stars, Eclipsing Binaries, Time Series Analysis, Data Reduction
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