Table 4.
Values for some of the derived parameters and the loglikelihood for the Bayesian MCMC fits with Eq. (6) to the moments of the studied absorption lines.
| Line | Moment | C | Arot, 1 | Arot, 2 | Apuls | fpuls | ℒ(Θ) | |
|---|---|---|---|---|---|---|---|---|
| (km s−1) | (km s−1) | (km s−1) | (km s−1) | (d−1) | ||||
| C II | 4267.3 Å | ⟨v⟩ | 0.7 ± 0.2 | 1.2 ± 0.2 | 1.7 ± 0.2 | 2.3 ± 0.2 | 1.10577 ± 0.00002 | −159.6 |
| ⟨v0⟩ | 10.2 ± 0.1 | 0.7 ± 0.2 | 0.4 ± 0.2 | 0.0 ± 0.2 | − | −133.1 | ||
| Mg II | 4481.1 Å | ⟨v⟩ | 0.4 ± 0.1 | 0.6 ± 0.2 | 0.8 ± 0.2 | 2.4 ± 0.2 | 1.10574 ± 0.00002 | −141.4 |
| ⟨v0⟩ | 14.9 ± 0.1 | 0.4 ± 0.2 | 0.1 ± 0.2 | 0.0 ± 0.2 | − | −133.1 | ||
| Fe II | 4549.5 Å | ⟨v⟩ | 1.7 ± 0.1 | 2.3 ± 0.2 | 2.0 ± 0.2 | 1.0 ± 0.1 | 1.10567 ± 0.00003 | −207.0 |
| ⟨v0⟩ | 5.8 ± 0.1 | 0.5 ± 0.2 | 0.3 ± 0.2 | 0.0 ± 0.2 | − | −134.6 | ||
| He I | 4713.2 Å | ⟨v⟩ | 0.9 ± 0.1 | 1.4 ± 0.2 | 1.5 ± 0.2 | 2.9 ± 0.2 | 1.10576 ± 0.00001 | −162.3 |
| ⟨v0⟩ | 9.2 ± 0.1 | 0.1 ± 0.2 | 0.1 ± 0.2 | 0.0 ± 0.2 | − | −131.8 | ||
| Si II | 6347.1 Å | ⟨v⟩ | 0.1 ± 0.1 | 1.1 ± 0.2 | 0.6 ± 0.2 | 1.5 ± 0.2 | 1.10570 ± 0.00002 | −140.6 |
| ⟨v0⟩ | 10.8 ± 0.1 | 1.3 ± 0.2 | 0.0 ± 0.2 | 0.0 ± 0.2 | − | −133.2 | ||
| H I | 6562.2 Å | ⟨v⟩ | 0.1 ± 0.3 | 0.0 ± 0.4 | 0.0 ± 0.3 | 0.8 ± 0.3 | 1.10575 ± 0.00009 | −33.4 |
| ⟨v0⟩ | 68.6 ± 0.2 | 0.1 ± 0.4 | 0.0 ± 0.3 | 0.0 ± 0.4 | − | −34.8 | ||
| He I | 6678.2 Å | ⟨v⟩ | 0.1 ± 0.2 | 0.6 ± 0.2 | 0.7 ± 0.2 | 2.9 ± 0.2 | 1.10575 ± 0.00003 | −159.7 |
| ⟨v0⟩ | 11.8 ± 0.1 | 0.1 ± 0.3 | 0.4 ± 0.2 | 0.1 ± 0.3 | − | −133.4 | ||
Notes. Results for the fits to either ⟨v⟩ or ⟨v0⟩ of a given absorption line to determine the amplitude of the periodic variability with frot and with fpuls. The former was kept fixed during the analysis. The amplitudes Arot, i correspond to variability with i × frot, the amplitude Apuls with the g-mode frequency fpuls and C is a constant off-set. No accurate values for fpuls were recovered during the fit to ⟨v0⟩ due to zero Apuls and flat PDFs for fpuls, demonstrating that the model of Eq. (6) is overfitting these data.
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