MIARMA: A minimal-loss information method for filling gaps in time series
Application to CoRoT light curves⋆
Instituto de Astrofísica de Andalucía – CSIC,
e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org
Received: 25 September 2014
Accepted: 11 December 2014
Context. Gaps in time series cause spurious frequencies in the power spectra. In light curves of pulsating stars, this hampers identifying the theoretical oscillation modes. When these gaps are smaller than the total time span, the commonly used approach to overcome these difficulties involves linear interpolation. The original frequency content of the pulsating stars is not preserved in the resulting time series.
Aims. The method presented here intends to minimize the effect of the gaps in the power spectra by gap-filling that preserves at best the original information, that is, the stellar oscillation frequency content for asteroseismology.
Methods. We used a forward-backward predictor based on autoregressive moving-average modelling (ARMA) in the time domain. The algorithm MIARMA is particularly suitable for replacing invalid data such as those present in the light curves of the CoRoT satellite due to the pass through the South Atlantic Anomaly, and eventually for the data gathered by the NASA planet hunter Kepler. We selected a sample of stars from the ultra-precise photometry collected by the asteroseismic camera on board the CoRoT satellite: the δ Scuti star HD 174966, showing periodic variations of the same order as the CoRoT observational window, the Be star HD 51193, showing longer time variations, and the solar-like HD 49933, with rapid time variations.
Results. We show that in some cases linear interpolations are less reliable than previously believed. The ARMA interpolation method provides a cleaner power spectrum, that is, less contaminated by spurious frequencies. In summary, MIARMA appears to be a suitable method for filling gaps in the light curves of pulsating stars observed by CoRoT since the method aims to preserve their frequency content, which is a necessary condition for asteroseismic studies.
Key words: asteroseismology / methods: data analysis / stars: oscillations
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