Poster abstract details

It is important in Helio- and astero-seismology to have continuous data. However, seismic observations can contain gaps and we need to take them into account. For instance, the time series of CoRoT data are periodically perturbed by high-energy particle hitting the satellite when it is crossing the South Atlantic Anomaly (SAA). The presence of repetitive gaps, which come from this perturbation, induces non-desirable peaks in the power spectrum.
To interpolate the repetitive gaps and reduce the presence of non-desirable peaks, we have adopted an interpolation technique which is called 'in-painting' algorithm.
First, we have tested this algorithm on the VIRGO data to which we had applied an artificial window to introduce gaps. We have optimized the algorithm to minimize the difference between the power spectrum density of the data with gaps and the complete time series.
We have also applied the algorithm to the CoRoT data. We have chosen two different runs: one of high quality and another one heavily perturbed by the SAA. We have perturbed the high-quality data with the SAA signature and compare the power spectrum of the new data to the original one.
We find that the power spectrum of the in-painted time series is almost as good as the original, unperturbed one. Seismic inferences obtained after interpolating the data are the same as in the original case.