Abstract
We introduce a new Python 1D chemical kinetic code, Full and Reduced Exoplanet Chemical Kinetics distiLLed (FRECKLL), to evolve large chemical networks efficiently. FRECKLL employs “distillation” in computing the reaction rates, which minimizes the error bounds to the minimum allowed by double precision values (ϵ ≤ 10−15). Compared to summation of rates with traditional algorithms like pairwise summation, distillation provides a tenfold reduction in solver time for both full and reduced networks. Both the full and reduced Venot2020 networks are packaged in FRECKLL as well as a TauREx 3.1 plug-in for usage in forward modeling and retrievals of exoplanet atmospheres. We present TauREx retrievals performed on a simulated HD 189733b JWST spectra using the full and reduced Venot2020 chemical networks and demonstrate the viability of total disequilibrium chemistry retrievals and the ability for JWST to detect disequilibrium processes.
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