PYMIEDAP: a Python–Fortran tool for computing fluxes and polarization signals of (exo)planets

Rossi, Loïc; Berzosa-Molina, J.; Stam, Daphné

doi:10.1051/0004-6361/201832859

Cited by 22 publications

(14 citation statements)

References 34 publications

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“…Like Earth, our model planets are covered by different surface types and have gaseous atmospheres that can contain liquid water clouds. Our radiative-transfer algorithm (Rossi et al 2018), computes the starlight that is reflected by a locally plane-parallel and horizontally homogeneous surfaceatmosphere model, where the atmosphere consists of a stack of homogeneous layers. To capture the spatial variation in surface-atmosphere models while adhering to the requirements of our radiative transfer algorithm, we describe each spherical planet with locally flat facets each of which is assigned a specific surface-atmosphere model.…”

Section: Phase Curves For Homogeneous Planetsmentioning

confidence: 99%

Exoplanet Cartography using Convolutional Neural Networks

Meinke,

Stam,

Visser

2022

Preprint

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Context. In the near-future, dedicated telescopes will observe Earth-like exoplanets in reflected parent-starlight, allowing their physical characterization. Because of the huge distances, every exoplanet will remain an unresolved, single pixel, but temporal variations in the pixel's spectral flux contain information about the planet's surface and atmosphere.Aims. We test convolutional neural networks for retrieving a planet's rotation axis, surface and cloud map from simulated single-pixel observations of flux and polarization light curves. We investigate the influence of assuming that the reflection by the planets is Lambertian in the retrieval while in reality their reflection is bidirectional, and of including polarization in retrievals.Methods. 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 six spectral bands from 400 to 800 nm, at various levels of photon noise. 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.Results. 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 facets and 85% of vegetation, desert, and cloud facets are correctly retrieved, in the absence of noise. With realistic amounts of 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.

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Section: Phase Curves For Homogeneous Planetsmentioning

confidence: 99%

Exoplanet Cartography using Convolutional Neural Networks

Meinke,

Stam,

Visser

2022

Preprint

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“…We pre-compute R of the local atmosphere-surface models (see Sect. 2.3) for each λ on a grid of 100 µ-points, 100-µ 0 points and a Fourier-series representation for the azimuth direction (see de Haan et al 1987;Rossi et al 2018, for details about the Fourier series expansion). We interpolate between those grids to obtain R i j at (δ i , ϑ j ) and λ for locally reflected light in the direction of the observer, and evaluate Eq.…”

Section: Computing the Reflected Starlightmentioning

confidence: 99%

Ocean signatures in the total flux and polarization spectra of Earth-like exoplanets

Trees,

Stam

2022

Preprint

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Context. Numerical simulations of starlight that is reflected by Earth-like exoplanets predict habitability signatures that can be searched for with future telescopes. Aims. We explore signatures of water oceans in the flux and polarization spectra of this reflected light. Methods. With an adding-doubling algorithm, we compute the total flux F, polarized flux Q and degree of polarization P s of starlight reflected by dry and ocean model planets with Earth-like atmospheres and patchy clouds. The oceans consist of Fresnel reflecting surfaces with wind-ruffled waves, foam and wave shadows, above natural blue seawater. Our results are presented as functions of wavelength (from 300 to 2500 nm with 1 nm resolution) and as functions of the planetary phase angle from 90 • to 170 • . Results. The ocean glint increases F, |Q| and P s with increasing phase angle at non-absorbing wavelengths, and causes the spectra of F and |Q| for the various phase angles to intersect. In the near-infrared, Q is negative, i.e. the direction of polarization is perpendicular to the plane through the star, planet, and observer. In the P s -spectra, the glint leaves dips (instead of peaks) in gaseous absorption bands. All those signatures are missing in the spectra of dry planets. Conclusions. The dips in P s , and the negative Q in the near-infrared, can be searched for at a phase angle of 90 • , where the planet-star separation is largest. Those ocean signatures in polarized light do not suffer from false positive glint signals that could be due to clouds or reflecting dry surfaces. For heavily cloudy planets, ocean detection is possible when the glint is (partially) cloud-free. When modelling signals of planets with oceans, using horizontally inhomogeneous cloud covers is thus crucial. Observations spread over time would increase the probability of catching a cloud-free glint and detecting an ocean.

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“…Since polarization simulations of starlight reflected by an exoplanet extending to the ultraviolet (UV), where P is believed to be particularly large, have never been performed before, we employed the PyMieDAP code [22] to show the exquisite sensitivity of UV polarization measurements to the physical properties of planetary atmospheres. Figure 3 shows flux (top row) and P (bottom row) as a function of phase angle at three UV wavelengths (150, 200, and 300 nm) for a HD189733b-like exoplanet hosting a clear atmosphere (left) and an atmosphere dominated by NH 3 (middle) or MgSiO 3 (enstatite; right) aerosols.…”

Section: Polarimetry In the Ultravioletmentioning

confidence: 99%

Spectropolarimetry as a tool for understanding the diversity of planetary atmospheres

et al. 2021

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The polarization state of starlight reflected by a planetary atmosphere uniquely reveals coverage, particle size, and composition of aerosols as well as changing cloud patterns. It is not possible to obtain a comparable level of detail from flux-only observations. It is therefore a powerful tool to better understand the crucial role played by clouds and aerosols in the chemistry, dynamics, and radiative balance of a planet. Furthermore, polarization observations can probe the atmosphere of planets independently of the orbital geometry (hence it applies to both transiting and non-transiting exoplanets). A high-resolution spectropolarimeter with a broad wavelength coverage, particularly if attached to a large space telescope, would enable simultaneous study of the polarimetric planetary properties of the continuum and to look for and characterize the polarimetric signal due to scattering from single molecules, providing detailed information about the composition and vertical structure of the atmosphere.

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PYMIEDAP: a Python–Fortran tool for computing fluxes and polarization signals of (exo)planets

Cited by 22 publications

References 34 publications

Exoplanet Cartography using Convolutional Neural Networks

Exoplanet Cartography using Convolutional Neural Networks

Ocean signatures in the total flux and polarization spectra of Earth-like exoplanets

Spectropolarimetry as a tool for understanding the diversity of planetary atmospheres

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