A framework for describing correlated excitation of solar p‐modes

Chaplin, W. J.; Elsworth, Y.; Toutain, T.

doi:10.1002/asna.200710977

Cited by 5 publications

(11 citation statements)

References 10 publications

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“…Having fixed the input i s , we then used the tabulated v i sin s and asteroseismic radius to determine the input s n . The power spectra were directly generated in the frequency domain, having preserved the same frequency resolution as in the real data and properly modeled the correlation between the background noise and the excitation function (see Chaplin et al 2008). The adopted mode linewidths and S/N were based on the observed linewidths and S/N of radial modes (not split by rotation).…”

Section: Appendix C Tests With Artificial Datamentioning

confidence: 99%

Spin–orbit Alignment of Exoplanet Systems: Ensemble Analysis Using Asteroseismology

Campante

Lund

Kuszlewicz

et al. 2016

ApJ

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104

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The angle ψ between a planet's orbital axis and the spin axis of its parent star is an important diagnostic of planet formation, migration, and tidal evolution. We seek empirical constraints on ψ by measuring the stellar inclination i s via asteroseismology for an ensemble of 25 solar-type hosts observed with NASA's Kepler satellite. Our results for i s are consistent with alignment at the 2σ level for all stars in the sample, meaning that the system surrounding the red-giant star Kepler-56 remains as the only unambiguous misaligned multiple-planet system detected to date. The availability of a measurement of the projected spin-orbit angle λ for two of the systems allows us to estimate ψ. We find that the orbit of the hot Jupiter HAT-P-7b is likely to be retrograde ( 116 . 4 14.730.2, whereas that of Kepler-25c seems to be well aligned with the stellar spin axis ( 12 . 6 11.0 6.7While the latter result is in apparent contradiction with a statement made previously in the literature that the multi-transiting system Kepler-25 is misaligned, we show that the results are consistent, given the large associated uncertainties. Finally, we perform a hierarchical Bayesian analysis based on the asteroseismic sample in order to recover the underlying distribution of ψ. The ensemble analysis suggests that the directions of the stellar spin and planetary orbital axes are correlated, as conveyed by a tendency of the host stars to display large values of inclination.

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Section: Appendix C Tests With Artificial Datamentioning

confidence: 99%

Spin–orbit Alignment of Exoplanet Systems: Ensemble Analysis Using Asteroseismology

Campante

Lund

Kuszlewicz

et al. 2016

ApJ

Self Cite

104

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“…This can be understood by considering the analogy of damped, stochastically forced oscillators. Modes of different frequencies will be 'kicked' by the common excitation at different phases in their oscillation cycles, and provided the frequencies differ by more than a few line widths (see Chaplin et al 2008) -a condition easily met by consecutive overtones of the low-l modes, which are separated by ∼ 135 µHz -there will be significant differences in how the amplitudes vary in time, due to the excitation.…”

Section: Correlated Noise and Mode Excitationmentioning

confidence: 99%

“…APPENDIX A: THE SOLARFLAG COMPLEX FREQUENCY AMPLITUDE, AND FREQUENCY POWER, SPECTRUM In the following description -which is based on the detailed discussions in Toutain et al (2006) Chaplin et al (2008) -we model the p-modes as forced, damped oscillators having a high Q. The frequency response of a given mode (with n, l and m) is then just a Lorentzian, which may be written in complex amplitude form as:…”

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confidence: 99%

Validation of solar-cycle changes in low-degree helioseismic parameters from the Birmingham Solar-Oscillations Network

Howe¹,

Davies²,

Chaplin³

et al. 2015

Mon. Not. R. Astron. Soc.

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We present a new and up-to-date analysis of the solar low-degree p-mode parameter shifts from the Birmingham Solar-Oscillations Network (BiSON) over the past 22 years, up to the end of 2014. We aim to demonstrate that they are not dominated by changes in the asymmetry of the resonant peak profiles of the modes and that the previously published results on the solar-cycle variations of mode parameters are reliable. We compare the results obtained using a conventional maximum likelihood estimation algorithm and a new one based on the Markov Chain Monte Carlo (MCMC) technique, both taking into account mode asymmetry. We assess the reliability of the solar-cycle trends seen in the data by applying the same analysis to artificially generated spectra. We find that the two methods are in good agreement. Both methods accurately reproduce the input frequency shifts in the artificial data and underestimate the amplitude and width changes by a small amount, around 10 per cent. We confirm earlier findings that the frequency and line width are positively correlated, and the mode amplitude anticorrelated, with the level of solar activity, with the energy supplied to the modes remaining essentially unchanged. For the mode asymmetry the correlation with activity is marginal, but the MCMC algorithm gives more robust results than the MLE.The magnitude of the parameter shifts is consistent with earlier work. There is no evidence that the frequency changes we see arise from changes in the asymmetry, which would need to be much larger than those observed in order to give the observed frequency shift.

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“…First, background noise is correlated with the excitation functions of the modes. Secondly, overtones of the same angular degree and azimuthal order have excitation functions that are correlated in time (see Chaplin, Elsworth & Toutain 2008a). In this framework, correlation of the excitation follows naturally from invoking correlations with the background noise.…”

Section: Introductionmentioning

confidence: 99%

solarFLAG hare and hounds: estimation of p-mode frequencies from Sun-as-star helioseismology data

Jiménez-Reyes

Chaplin

García

et al. 2008

Monthly Notices of the Royal Astronomical Society

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We report on the results of the latest solarFLAG hare-and-hounds exercise, which was concerned with testing methods for extraction of frequencies of low-degree solar p modes from data collected by Sun-as-a-star observations. We have used the new solarFLAG simulator, which includes the effects of correlated mode excitation and correlations with background noise, to make artificial time-series data that mimic Doppler velocity observations of the Sun-as-a-star. The correlations give rise to asymmetry of mode peaks in the frequency power spectrum. 10 members of the group (the hounds) applied their 'peak-bagging' codes to a 3456-d data set, and the estimated mode frequencies were returned to the hare (who was WJC) for comparison. Analysis of the results reveals a systematic bias in the estimated frequencies of modes above ≈1.8 mHz. The bias is negative, meaning the estimated frequencies systematically underestimate the input frequencies.We identify two sources that are the dominant contributions to the frequency bias. Both sources involve failure to model accurately subtle aspects of the observed power spectral density in the part (window) of the frequency power spectrum that is being fitted. One source of bias arises from a failure to account for the power spectral density coming from all those modes whose frequencies lie outside the fitting windows. The other source arises from a failure to account for the power spectral density of the weak l = 4 and 5 modes, which are often ignored in Sun-as-a-star analysis. The Sun-as-a-star peak-bagging codes need to allow for both sources, otherwise the frequencies are likely to be biased.

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A framework for describing correlated excitation of solar p‐modes

Cited by 5 publications

References 10 publications

Spin–orbit Alignment of Exoplanet Systems: Ensemble Analysis Using Asteroseismology

Spin–orbit Alignment of Exoplanet Systems: Ensemble Analysis Using Asteroseismology

Validation of solar-cycle changes in low-degree helioseismic parameters from the Birmingham Solar-Oscillations Network

solarFLAG hare and hounds: estimation of p-mode frequencies from Sun-as-star helioseismology data

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