2020A&A...642A.226A

Context. The existence of mixed modes in stars is a marker of stellar evolution. Their detection serves for a better determination of stellar age.
Aims. The goal of this paper is to identify the dipole modes in an automatic manner without human intervention.
Methods. I used the power spectra obtained by the Kepler mission for the application of the method. I computed asymptotic dipole mode frequencies as a function of the coupling factor and dipole period spacing, as well as other parameters. For each star, I collapsed the power in an echelle diagramme aligned onto the monopole and dipole mixed modes. The power at the null frequency was used as a figure of merit. Using a genetic algorithm, I then optimised the figure of merit by adjusting the location of the dipole frequencies in the power spectrum. Using published frequencies, I compared the asymptotic dipole mode frequencies with published frequencies. I also used published frequencies to derive the coupling factor and dipole period spacing using a non-linear least squares fit. I used Monte-Carlo simulations of the non-linear least square fit to derive error bars for each parameter.
Results. From the 44 subgiants studied, the automatic identification allows one to retrieve within 3 µHz, at least 80% of the modes for 32 stars, and within 6 µHz, at least 90% of the modes for 37 stars. The optimised and fitted gravity-mode period spacing and coupling factor are in agreement with previous measurements. Random errors for the mixed-mode parameters deduced from the Monte-Carlo simulation are about 30-50 times smaller than previously determined errors, which are in fact systematic errors.
Conclusions. The period spacing and coupling factors of mixed modes in subgiants are confirmed. The current automated procedure will need to be improved upon using a more accurate asymptotic model and/or proper statistical tests.