Exoplanet Cartography using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks can effectively retrieve exoplanet surface and atmospheric maps from single-pixel spectral and polarization data, even with realistic noise levels, improving understanding of distant Earth-like planets.

Contribution

The study introduces a neural network approach that accounts for bidirectional reflection and polarization, enhancing exoplanet surface mapping accuracy from limited observational data.

Findings

Neural networks can constrain exoplanet rotation axes with low mean squared error.

Including polarization improves map feature retrieval accuracy.

Networks trained on Lambertian models perform poorly on bidirectional reflection planets without polarization data.

Abstract

In the near-future, dedicated telescopes observe Earth-like exoplanets in reflected light, allowing their characterization. Because of the huge distances, every exoplanet will be a single pixel, but temporal variations in its spectral flux hold information about the planet's surface and atmosphere. We test convolutional neural networks for retrieving a planet's rotation axis, surface and cloud map from simulated single-pixel flux and polarization observations. We investigate the assumption that the planets reflect Lambertian in the retrieval while their actual reflection is bidirectional, and of including polarization in retrievals. We simulate observations along a planet's orbit using a radiative transfer algorithm that includes polarization and bidirectional reflection by vegetation, desert, oceans, water clouds, and Rayleigh scattering in 6 spectral bands from 400 to 800 nm, at various photon noise levels. The surface-types and cloud patterns of the facets covering a model planet are based on probability distributions. Our networks are trained with simulated observations of millions of planets before retrieving maps of test planets. The neural networks can constrain rotation axes with a mean squared error (MSE) as small as 0.0097, depending on the orbital inclination. On a bidirectionally reflecting planet, 92% of ocean and 85% of vegetation, desert, and cloud facets are correctly retrieved, in the absence of noise. With realistic noise, it should still be possible to retrieve the main map features with a dedicated telescope. Except for face-on orbits, a network trained with Lambertian reflecting planets, yields significant retrieval errors when given observations of bidirectionally reflecting planets, in particular, brightness artefacts around a planet's pole. Including polarization improves retrieving the rotation axis and the accuracy of the retrieval of ocean and cloud facets.