Occultation mapping of Io's surface in the near-infrared I: Inferring static maps

TL;DR

This paper presents a Bayesian modeling approach to infer static surface maps of Io from occultation light curves, successfully recovering known volcanic hotspots with minimal data and assumptions, with broader applications to stellar and exoplanet surface mapping.

Contribution

We developed a fast, analytic, and differentiable generative model for occultation light curves that enables surface mapping without prior assumptions on spot locations or shapes.

Findings

Successfully recovered known hotspots on Io using only two light curves.

The model is applicable to mapping surfaces of stars and exoplanets.

Provides a new method for surface characterization from occultation data.

Abstract

Jupiter's moon Io is the most volcanically active body in the Solar System with hundreds of active volcanoes varying in intensity on different timescales. Io has been observed during occultations by other Galilean moons and Jupiter since the 1980s, using high-cadence near infrared photometry. These observations encode a wealth of information about the volcanic features on its surface. We built a generative model for the observed occultations using the code starry which enables fast, analytic, and differentiable computation of occultation light curves in emitted and reflected light. Our probabilistic Bayesian model is able to recover known hotspots on the surface of Io using only two light curves and without any assumptions on the locations, shapes or the number of spots. The methods we have developed are also directly applicable to the problem of mapping the surfaces of stars and exoplanets.