The ARCiS framework for exoplanet atmospheres

Modelling philosophy and retrieval^★,★★

¹ SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
e-mail: M.Min@sron.nl
² Department of Astronomy, Tsinghua University, Beijing 100084, PR China
³ Centre for Exoplanet Science, University of St Andrews, Nort Haugh, St Andrews, KY169SS, UK
⁴ SUPA, School of Physics & Astronomy, University of St Andrews, St Andrews, KY16 9SS, UK

Received: 20 December 2019
Accepted: 22 June 2020

Abstract

Aims. We present ARCiS, a novel code for the analysis of exoplanet transmission and emission spectra. 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 leaving certain parts of the model, where our physical understanding remains limited, free to vary. This approach, in between full physical modelling and full parameterisation, allows us to use the processes we understand well and parameterise those less understood. We implemented a Bayesian retrieval framework and applied it to the transit spectra of a set of ten 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 JWST or the ESA Ariel missions for example. 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, their elemental abundance ratios cannot be reliably constrained based on the current dataset. 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 those for which 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.