TL;DR
This paper introduces exocartographer, a Bayesian framework that reconstructs exoplanet surface maps and their uncertainties from light curves, demonstrating its effectiveness with Earth-like simulations.
Contribution
The paper presents exocartographer, a novel open-source Bayesian method for 2D exoplanet surface mapping with uncertainty quantification, advancing beyond previous low-resolution and spin constraint techniques.
Findings
Successfully retrieved Earth-like albedo maps with 30% uncertainty.
Accurately constrained planetary obliquity within 0.8 degrees.
Identified key surface features like Sahara desert and Pacific Ocean.
Abstract
Future space telescopes will directly image extrasolar planets at visible wavelengths. Time-resolved reflected light from an exoplanet encodes information about atmospheric and surface inhomogeneities. Previous research has shown that the light curve of an exoplanet can be inverted to obtain a low-resolution map of the planet, as well as constraints on its spin orientation. Estimating the uncertainty on 2D albedo maps has so far remained elusive. Here we present exocartographer, a flexible open-source Bayesian framework for solving the exo-cartography inverse problem. The map is parameterized with equal-area HEALPix pixels. For a fiducial map resolution of 192 pixels, a four-parameter Gaussian process describing the spatial scale of albedo variations, and two unknown planetary spin parameters, exocartographer explores a 198-dimensional parameter space. To test the code, we produce a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
