Atmospheric Library of Far Away Worlds

The grid is divided into two major condensation types, rainout condensation and local condensation. Then for each condensation method there are two tar files one containing model transmission spectra and the other model chemical abundances. A python code to scale generic grid model to wide range of planet-star combinations can be found in Utilities/grid_scaling_code folder.The article associated with this dataset is located in ORE at: http://hdl.handle.net/10871/34674Simulated exoplanet transmission spectra are critical for planning and interpretation of observations and to explore the sensitivity of spectral features to atmospheric thermochemical processes. We present a publicly available generic model grid of planetary transmission spectra, scalable to a wide range of H$_2$/He dominated atmospheres. The grid is computed using the 1D/2D atmosphere model ATMO for two different chemical scenarios, first considering local condensation only, secondly considering global condensation and removal of species from the atmospheric column (rainout). The entire grid consists of 56,320 model simulations across 22 equilibrium temperatures (400 - 2600 K), four planetary gravities (5 - 50 ms$^{-2}$), five atmospheric metallicities (1x - 200x), four C/O ratios (0.35 - 1.0), four scattering haze parameters, four uniform cloud parameters, and two chemical scenarios. We derive scaling equations which can be used with this grid, for a wide range of planet-star combinations. We validate this grid by comparing it with other model transmission spectra available in the literature. We highlight some of the important findings, such as the rise of SO$_2$ features at 100x solar metallicity, differences in spectral features at high C/O ratios between two condensation approaches, the importance of VO features without TiO to constrain the limb temperature and features of TiO/VO both, to constrain the condensation processes. Finally, this generic grid can be used to plan future observations using the HST, VLT, JWST and various other telescopes. The fine variation of parameters in the grid also allows it to be incorporated in a retrieval framework, with various machine learning techniques.Jayesh Goyal and Nathan Mayne are part funded by a Leverhulme Trust Research Project Grant, and in part by a University of Exeter College of Engineering, Mathematics and Physical Sciences PhD studentship. Hannah Wakeford acknowledges funding by Association of Universities for Research in Astronomy (AURA) through a Giacconi Fellowship appointed at Space Telescope Science Institute (STScI). David Sing and Benjamin Drummond acknowledge support from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ ERC grant agreement number 336792. We acknowledge support from the Space Telescope Science Institute (STScI) Research Visitors Program. This work used the DiRAC Complexity system, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility. This work also used the University of Exeter Supercomputer, a DiRAC Facility jointly funded by STFC, the Large Facilities Capital Fund of BIS and the University of Exeter