Massively parallel WRNN reconstructors for spectrum recovery in astronomical photometrical surveys

The investigation of solar-like oscillations for probing star interiors has enjoyed a tremendous growth in the last decade. Once observations are over, the most notable difficulties in properly identifying the true oscillation frequencies of stars are due to the gaps in the observation time-series and the intrinsic stellar granulation noise. This paper presents an innovative neuro-wavelet reconstructor for the missing data of photometric signals. Firstly, gathered data are transformed using wavelet operators and filters, and this operation removes granulation noise, then we predict missing data by a composite of two neural networks, which together allow a "forward and backward" reconstruction. This resulting error is greatly lower than the absolute a priori measurement error. The devised reconstruction approach gives a signal that is better suited to be Fourier transformed when compared with other existing methods.