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 (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 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.
@article{arxiv.2209.11203,
title = {FRECKLL: Full and Reduced Exoplanet Chemical Kinetics distiLLed},
author = {Ahmed Faris Al-Refaie and Olivia Venot and Quentin Changeat and Billy Edwards},
journal= {arXiv preprint arXiv:2209.11203},
year = {2024}
}