Application of cubic splines to the spectral analysis of unequally spaced data

Details Application of cubic splines to the spectral analysis of unequally spaced data Application of cubic splines to the spectral analysis of unequally spaced data

Dec 1994

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994apj...436..787a&link_type=abstract

Astrophysical Journal, Part 1 (ISSN 0004-637X), vol. 436, no. 2, p. 787-794

Statistics

Applications

18

Astronomical Spectroscopy, Cubic Equations, Data Processing, Data Structures, Least Squares Method, Spectroscopic Analysis, Spline Functions, Statistical Analysis, Variable Stars, Algorithms, Applications Programs (Computers), Fast Fourier Transformations, Light Curve

Scientific paper

In the absence of a priori information, nonparametric statistical techniques are often useful in exploring the structure of data. A least-squares fitting program, based on cubic B-splines has been developed to analyze the periodicity of variable star light curves. This technique takes advantage of the limited domain within which a particular B-spline is nonzero to substantially reduce the number of calculations needed to generate the regression matrix. By using simple approximations adapted to modern computer workstations, the computational speed is competitive with most other common methods that have been described in the literature. Since the number of arithmetic operations increases as N2, where N is the number of data points, this method cannot compete with the fast Fourier transformations (FFT) modification of the Lomb-Scargle algorithm. However, for data sets with N less than 104, it should be quite useful. Examples are shown, taken from the Massive Compact Halo Object (MACHO) experiment.

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