A JWST Transit of a Jupiter Analog I: Constraints on the Oblateness of Kepler-167e

TL;DR

This study analyzes JWST transit data of Kepler-167e to constrain its oblateness, finding that current data cannot definitively determine oblateness but sets upper bounds and highlights the need for improved noise mitigation.

Contribution

It introduces a new independent data reduction pipeline and compares multiple modeling approaches to constrain the oblateness of a Jupiter-analog exoplanet.

Findings

95% upper bound on oblateness: f<0.097

Constraints depend on data reduction pipeline and systematics

Further noise mitigation needed for tighter constraints

Abstract

In October 2024 JWST observed a transit of Kepler-167e, a Jupiter-analog planet on a 1000+ day orbit. These observations, recorded over a long baseline of nearly 60 hours, were designed to search for signatures of planetary oblateness and/or exomoons comparable to Ganymede. In this first in a series of studies analyzing these data we report on constraints on Kepler-167e's oblateness. We explored a large grid of data reduction pipelines and modeling choices, including a new entirely independent reduction pipeline ("katahdin") and two new treatments for limb darkening. We find that under a Bayesian model comparison framework the data are fit equally well by both spherical and oblate planet models, and that our ability to constrain the oblateness is negatively impacted by the influence of exposure-long trends. Using the most conservative of our posteriors, we place a 95% upper bound on the projected oblateness of $f<0.097$, which corresponds to a rotation period of $P\geq7.11$ hours if the planet's spin axis is aligned with the sky plane. We note, however, that the final bound depends on the choice of reduction pipeline and systematics model, and that our suite of end-to-end analyses produced bounds as low as $f<0.065$ at 95%. We conclude that leveraging JWST to make tighter constraints on planetary oblateness will require further investigation into mitigating exposure-long trends and correlated noise.