The ExoMolOP database : cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres
Abstract
Here we present a publicly available database of opacities for molecules of astrophysical interest named ExoMolOP that has been compiled for over 80 species, and is based on the latest line list data from the ExoMol, HITEMP, and MoLLIST databases. These data are generally suitable for characterising high-temperature exoplanet or cool stellar and substellar atmospheres, and have been computed at a variety of pressures and temperatures, with a few molecules included at room temperature only from the HITRAN database. The data are formatted in di fferent ways for four di fferent exoplanet atmosphere retrieval codes; ARCiS, TauREx, NEMESIS, and petitRADTRANS, and include both cross sections (at R = λ/Δλ = 15 000) and k-tables (at R = λ/Δλ = 1000) for the 0.3-50 μm wavelength region. Opacity files can be downloaded and used directly for these codes. Atomic data for alkali metals Na and K are also included, using data from the NIST database and the latest line shapes for the resonance lines. Broadening parameters have been taken from the literature where available, or have been estimated from the parameters of a known molecule with similar molecular properties where no broadening data are available.
Citation
Chubb , K L , Rocchetto , M , Yurchenko , S N , Min , M , Waldmann , I , Barstow , J K , Molliere , P , Al-Refaie , A F , Phillips , M W & Tennyson , J 2021 , ' The ExoMolOP database : cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres ' , Astronomy & Astrophysics , vol. 646 , 21 . https://doi.org/10.1051/0004-6361/202038350
Publication
Astronomy & Astrophysics
Status
Peer reviewed
ISSN
0004-6361Type
Journal article
Rights
Copyright © 2021 ESO. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at https://doi.org/10.1051/0004-6361/202038350 .
Description
Funding: This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement 776403, and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 758892, ExoAI. P.M. acknowledges support from the European Research Council under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 832428. J.T. and S.Y. thank the STFC Project No. ST/R000476/1.Collections
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