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Predicting exoplanet observability in time, contrast, separation and polarization, in scattered light

Polarimetry is one of the keys to enhanced direct imaging of exoplanets. Not only does it deliver a differential observable providing extra contrast, but when coupled with spectroscopy, it also reveals valuable information on the exoplanetary atmospheric composition. Nevertheless, angular separation and contrast ratio to the host-star make for extremely challenging observation. Producing detailed predictions for exactly how the expected signals should appear is of critical importance for the designs and observational strategies of tomorrow's telescopes. We aim at accurately determining the magnitudes and evolution of the main observational signatures for imaging an exoplanet: separation, contrast ratio to the host-star and polarization as a function of the orbital geometry and the reflectance parameters of the exoplanet. These parameters were used to construct polarized-reflectance model based on the input of orbital parameters and two albedo values. The model is able to calculate a variety of observational predictions for exoplanets at any orbital time. The inter-dependency of the three main observational criteria -separation, contrast ratio, polarization- result in a complex time-evolution of the system. They greatly affect the viability of planet observation by direct imaging. We introduce a new generic display of the main observational criteria, which enables an observer to determine whether an exoplanet is within detection limits: the Separation-POlarization-Contrast diagrams (SPOC). We explore the complex effect of orbital and albedo parameters on the visibility of an exoplanet. The code we developed is available for public use and collaborative improvement on the python package index, together with its documentation. It is another step towards a full comprehensive simulation tool for predicting and interpreting the results of future observational exoplanetary discovery campaigns.

preprint2015arXivOpen access

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