On attempting to automate the identification of mixed dipole modes for subgiant stars
Abstract
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.
- Publication:
-
Astronomy and Astrophysics
- Pub Date:
- October 2020
- DOI:
- 10.1051/0004-6361/202038834
- arXiv:
- arXiv:2008.10973
- Bibcode:
- 2020A&A...642A.226A
- Keywords:
-
- asteroseismology;
- methods: data analysis;
- stars: interiors;
- Astrophysics - Solar and Stellar Astrophysics
- E-Print:
- 18 pages, 20 figures, accepted in Astronomy and Astrophysics, 24 August 2020