A Hierarchical Bayesian Framework for Inferring the Stellar Obliquity Distribution
Jiayin Dong, Daniel Foreman-Mackey

TL;DR
This paper presents a hierarchical Bayesian framework to infer the distribution of stellar obliquities using only sky-projected measurements, revealing most hot Jupiters are aligned with their host stars.
Contribution
The introduced Bayesian method allows population-level obliquity inference without requiring stellar inclination constraints, improving analysis of exoplanet system architectures.
Findings
Most hot Jupiters are aligned with their host stars.
The obliquity distribution is unimodal and peaked at zero degrees.
Diverse obliquities suggest dynamic formation mechanisms.
Abstract
Stellar obliquity, the angle between a planet's orbital axis and its host star's spin axis, traces the formation and evolution of a planetary system. In transiting exoplanet observations, only the sky-projected stellar obliquity can be measured, but this can be de-projected using an estimate of the stellar obliquity. In this paper, we introduce a flexible, hierarchical Bayesian framework that can be used to infer the stellar obliquity distribution solely from sky-projected stellar obliquities, including stellar inclination measurements when available. We demonstrate that while a constraint on the stellar inclination is crucial for measuring the obliquity of an individual system, it is not required for robust determination of the population-level stellar obliquity distribution. In practice, the constraints on the stellar obliquity distribution are mainly driven by the sky-projected…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research
