Exploring the Detectability of Disequilibrium Biosignature Gas Pairs

In analyzing the detectability of life on nearby exoplanets, we explore a vital ensuing step: the detectability of disequilibrium biosignature gas pairings O2+CH4 and CH4+CO2. We do this for the different inhabited Eons of Earth history: Phanerozoic, Proterozoic, and Archean, and study each for a variety of stellar types. These gas pairings can suggest the presence of life on a planet; when simultaneously detected, they indicate gas production rates too rapid to be plausibly explained by abiotic processes. Arney 2019 and Segura et al. 2005 suggest that the O2+CH4 gas pair may be more detectable around M- and K-type stars than the Sun and the G-type stars. The changes in photochemistry lead to increased detectability, driven by differences in the UV fluxes from the respective star types. The updated analysis will establish the parameter space in which biological fluxes of O2+CH4 and CH4+CO2 are detectable for planets orbiting FGKM stars to evaluate which star-planet pairings offer the best outlook for increasing confidence in future exoplanet interpretations. This includes specific considerations for ruling out biosignature false-positives. We use a one-dimensional atmospheric model to calculate self-consistent gas concentrations and temperature profiles, for a range of abiotic and biotic flux rates spanning biotic and abiotic fluxes for each gas pair. This will help determine what gas pairs will be good candidates for biosignatures on exoplanets analogous to Earth. These simulations will establish a statistical population to create quantitative parameters for the detectability of disequilibrium biosignature pairs. Comparisons will be drawn on previous analyses of disequilibrium biosignature pairs (e.g. Arney 2019; Krissansen-Totton et al. 2018). In addition to being useful for biosignature/false-positive discrimination, the results of these simulations are also useful for “decision trees” for future telescope mission concepts. The goal of such decision trees is to optimally sort observed planets into three categories: (1) inhabited worlds, (2) worlds without global surface biospheres, and (3) ambiguous cases. This study represents the next step toward understanding the probability of detecting the presence of life on nearby exoplanets from remote observations.