• DOI:10.1051/0004-6361/201732431
• Corpus ID: 119077731

Impact of gaps in the asteroseismic characterization of pulsating stars

@article{PascualGranado2018ImpactOG,
  title={Impact of gaps in the asteroseismic characterization of pulsating stars},
  author={Javier Pascual-Granado and Juan Carlos Su'arez and Rafael Garrido and Andr{\'e}s Moya and A. Garc'ia Hern'andez and J. R. Rod'on and M Lares-Martiz},
  journal={Astronomy \& Astrophysics},
  year={2018}
}

• J. Pascual-Granado, J. Su'arez, +4 authors M. Lares-Martiz
• Published 8 January 2018
• Geology, Physics
• Astronomy & Astrophysics

Context. It is known that the observed distribution of frequencies in CoRoT and Kepler δ Scuti stars has no parallelism with any theoretical model. Pre-whitening is a widespread technique in the analysis of time series with gaps from pulsating stars located in the classical instability strip, such as δ Scuti stars. However, some studies have pointed out that this technique might introduce biases in the results of the frequency analysis. Aims. This work aims at studying the biases that can result from pre-whitening in asteroseismology. The results will depend on the intrinsic range and distribution of frequencies of the stars. The periodic nature of the gaps in CoRoT observations, only in the range of the pulsational frequency content of the δ Scuti stars, is shown to be crucial to determining their oscillation frequencies, the first step in performing asteroseismology of these objects. Hence, here we focus on the impact of pre-whitening on the asteroseismic characterization of δ Scuti stars.
Methods. We select a sample of 15 δ Scuti stars observed by the CoRoT satellite, for which ultra-high-quality photometric data have been obtained by its seismic channel. In order to study the impact on the asteroseismic characterization of δ Scuti stars we perform the pre-whitening procedure on three datasets: gapped data, linearly interpolated data, and data with gaps interpolated using Autoregressive and Moving Average models (ARMA).
Results. The different results obtained show that at least in some cases pre-whitening is not an efficient procedure for the deconvolution of the spectral window. Therefore, in order to reduce the effect of the spectral window to a minimum, in addition to performing a pre-whitening of the data, it is necessary to interpolate with an algorithm that is aimed to preserve the original frequency content.