Bayesian rotation inversion of KIC 11145123

Abstract

A scheme of Bayesian rotation inversion, which allows us to compute the probability of a model of a stellar rotational profile, is developed. The validation of the scheme with simple rotational profiles and the corresponding sets of artificially generated rotational shifts has been successfully carried out, and we can correctly distinguish the (right) rotational model, prepared beforehand for generating the artificial rotational shifts, with the other (wrong) rotational model. The Bayesian scheme is applied to a gamma Dor-delta Sct type hybrid star, KIC 11145123, leading to a result that the convective core of the star might be rotating much faster (~ 10 times faster) than the other regions of the star. The result is consistent with that previously suggested by Hatta et al. (2019) based on a 3-zone modeling, further strengthening their argument from a Bayesian point of view.

13 pages, 12 figures, 3 tables, accepted for publication in ApJ

References in corpus (11)

• Slowing the Spins of Stellar Cores
• Asteroseismic measurement of surface-to-core rotation in a main sequence A star, KIC 11145123
• Asteroseismic measurement of slow, nearly-uniform surface-to-core rotation in the main sequence F star KIC 9244992
• Signatures of internal rotation discovered in the Kepler data of five slowly pulsating B stars
• Constraining the efficiency of angular momentum transport with asteroseismology of red giants: the effect of stellar mass
• Results of a Search for gamma Dor and delta Sct Stars with the Kepler Spacecraft
• Rotation of the convective core in $\gamma$ Dor stars measured by dips in period spacings of g modes coupled with inertial modes
• Gravity-mode period spacings and near-core rotation rates of 611 $\gamma$ Doradus stars with \textit{Kepler}
• Seismic evidence for near solid-body rotation in two Kepler subgiants and implications for angular momentum transport
• Shape of a slowly rotating star measured by asteroseismology
• Inferring solar differential rotation through normal-mode coupling using Bayesian statistics