FRECKLL: Full and Reduced Exoplanet Chemical Kinetics distiLLed

Abstract

We introduce a new Python 1D chemical kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) 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 ($\epsilon \leq 10^{-15}$). 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 plugin for usage in forward modelling and retrievals of exoplanet atmospheres. We present TauREx retrievals performed on a simulated HD189733 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.