Precomputed aerosol extinction, scattering, and asymmetry grids for scalable atmospheric retrievals

Context. The unprecedented wavelength coverage and sensitivity of the James Webb Space Telescope (JWST) permits us to measure the absorption features of a wide range of condensate species from silicates to Titan tholins. Atmospheric retrievals are uniquely suited for analyzing these datasets and characterizing the aerosols present in exoplanet atmospheres. However, including the optical properties of condensed particles within retrieval frameworks remains computationally expensive, which limits our ability to fully exploit JWST observations. Aims. We improve the computational efficiency and scaling behavior of aerosol models in atmospheric retrievals. This enables indepth studies that include multiple condensate species within practical timescales. Methods. Instead of computing the aerosol Mie coefficients for each sampled model, we precomputed the extinction efficiency (Qext), the scattering efficiency (Qscat), and the asymmetry parameter (g) grids for seven condensate species relevant in exoplanet atmospheres (Mg2SiO4 amorph sol - gel, MgSiO3 amorph glass, MgSiO3 amorph sol - gel, SiO2 alpha, SiO2 amorph, and SiO and Titan tholins). During the retrievals, the relevant values were obtained via linear interpolation between grid points. This reduced the computation times drastically. Results. The precomputed Qext grids significantly reduce the computation time by between 1.4 and 17 times, with negligible differences on the retrieved parameters. They also scale effortlessly with the number of aerosol species while maintaining the accuracy of cloud models, thereby enabling more complex retrievals as well as broader population studies without increasing the overall error budget. The Qext, Qscat, and g grids are freely available on Zenodo, as is a public TAUREX plugin (TAUREX-PCQ) that uses them. With their speed-ups, the aerosol grids presented in this paper are a significant step forward to handle both high information content and population-level datasets.