The ARCiS framework for Exoplanet Atmospheres: Modelling Philosophy and Retrieval
| Autor: | Christiane Helling, Michiel Min, Katy L. Chubb, Chris W. Ormel, Yui Kawashima |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Bayesian probability FOS: Physical sciences Context (language use) Astrophysics computer.software_genre 01 natural sciences Set (abstract data type) Planet 0103 physical sciences Hot Jupiter 010303 astronomy & astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) 0105 earth and related environmental sciences Physics Earth and Planetary Astrophysics (astro-ph.EP) Physical model Astronomy and Astrophysics Exoplanet Transmission (telecommunications) Space and Planetary Science Data mining Astrophysics::Earth and Planetary Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics computer Astrophysics - Earth and Planetary Astrophysics |
| Popis: | Aims: ARCiS, a novel code for the analysis of exoplanet transmission and emission spectra is presented. The aim of the modelling framework is to provide a tool able to link observations to physical models of exoplanet atmospheres. Methods: The modelling philosophy chosen in this paper is to use physical and chemical models to constrain certain parameters while keeping free the parts where our physical understanding is still more limited. This approach, in between full physical modelling and full parameterisation, allows us to use the processes we understand well and parameterise those less understood. A Bayesian retrieval framework is implemented and applied to the transit spectra of a set of 10 hot Jupiters. The code contains chemistry and cloud formation and has the option for self consistent temperature structure computations. Results: The code presented is fast and flexible enough to be used for retrieval and for target list simulations for e.g. JWST or the ESA Ariel missions. We present results for the retrieval of elemental abundance ratios using the physical retrieval framework and compare this to results obtained using a parameterised retrieval setup. Conclusions: We conclude that for most of the targets considered the current dataset is not constraining enough to reliably pin down the elemental abundance ratios. We find no significant correlations between different physical parameters. We confirm that planets in our sample with a strong slope in the optical transmission spectrum are the planets where we find cloud formation to be most active. Finally, we conclude that with ARCiS we have a computationally efficient tool to analyse exoplanet observations in the context of physical and chemical models. Accepted for publication in A&A |
| Databáze: | OpenAIRE |
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