Curvelet analysis of asteroseismic data I: Method description and application to simulated sun-like stars

Abstract

Context: The detection and identification of oscillation modes (in terms of their $\ell$, $m$ and successive $n$) is a great challenge for present and future asteroseismic space missions. The "peak tagging" is an important step in the analysis of these data to provide estimations of stellar oscillation mode parameters, i.e., frequencies, rotation rates, and further studies on the stellar structure. Aims: To increase the signal-to-noise ratio of the asteroseismic spectra computed from time series representative of MOST and CoRoT observations (30- and 150-day observations). Methods: We apply the curvelet transform -- a recent image processing technique which looks for curved patterns -- to echelle diagrams built using asteroseismic power spectra. In this diagram the eigenfrequencies appear as smooth continuous ridges. To test the method we use Monte Carlo simulations of several sun-like stars with different combinations of rotation rates, rotation-axis inclination and signal-to-noise ratios. Results: The filtered diagrams enhance the contrast between the ridges of the modes and the background allowing a better tagging of the modes and a better extraction of some stellar parameters. Monte Carlo simulations have also shown that the region where modes can be detected is enlarged at lower and higher frequencies compared to the raw spectra. Even more, the extraction of the mean rotational splitting from modes at low frequency can be done more easily than using the raw spectrum.