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Automated extraction of oscillation parameters for Kepler observations of solar-type stars
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The recent launch of the Kepler space telescope brings the opportunity to study oscillations systematically in large numbers of solar-like stars. In the framework of the asteroFLAG project, we have developed an automated pipeline to estimate global oscillation parameters, such as the frequency of maximum power (nu_max) and the large frequency spacing (Delta_nu), for a large number of time series. We present an effective method based on the autocorrelation function to find excess power and use a scaling relation to estimate granulation timescales as initial conditions for background modelling. We derive reliable uncertainties for nu_max and Delta_nu through extensive simulations. We have tested the pipeline on about 2000 simulated Kepler stars with magnitudes of V~7-12 and were able to correctly determine nu_max and Delta_nu for about half of the sample. For about 20%, the returned large frequency spacing is accurate enough to determine stellar radii to a 1% precision. We conclude that the methods presented here are a promising approach to process the large amount of data expected from Kepler.
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Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match know...
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