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Biosignature Gas Pairs: Detectability and Implications for Future Observing Strategies

Presentation #0202 in the session “Identifying Biosignatures”.

Published onMar 17, 2021
Biosignature Gas Pairs: Detectability and Implications for Future Observing Strategies

In this study, we explore a critical next step for understanding the detectability of life on nearby exoplanets: 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 because when simultaneously detected, they indicate gas production rates too rapid to be plausibly explained by abiotic processes. Previous analyses (Arney 2019; Segura et al. 2005) suggest that the O2+CH4 gas pair may be more detectable around M- and K-type stars than G-type stars such as the Sun. This increased detectability is due to changes in photochemistry, driven by differences in the UV fluxes from the respective star types. Our 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 1D atmospheric model to calculate self-consistent gas concentrations and temperature profiles, for a range of abiotic and biotic flux rates. This will help determine what gas pairs will be good candidates for biosignatures on exoplanets analogous to Earth. Preliminary results are in hand for the O2+CH4 atmospheres and are consistent previous results (Arney 2019; Segura et al. 2005). These initial findings suggest F, K, and M-type stars all exhibit higher CH4 concentrations when compared to G-type stars, with M-dwarfs exhibiting the highest amount of CH4 overall. For the K-dwarf (Arney 2019) and M-dwarf (Segura et al. 2005), this is because they photolyze ozone less readily than a G-type star (like the Sun) would. This creates a planetary environment in which fewer methane-destroying oxygen radicals are created, thereby allowing methane to build up without being rapidly destroyed as quickly. Thus, the O2+CH4 disequilibrium pair may be more readily detectable around these other types of stars. Planetary atmospheres simulated around F- and K-type stars exhibit similar trends to what we expect to see for G-type stars for methane concentrations in a Modern Earth setting. The F-type star case also shows slightly higher CH4 concentrations than the G-type star case. Our preliminary hypothesis is that the F-type star generates sufficient O3 to shield lower-atmospheric CH4 and H2O from photolysis, thereby slowing CH4 destruction rates and increasing CH4 concentrations. Simulations for a planet orbiting an M-type star suggest that, for a given CH4 flux, CH4 concentration increases with increasing O2 concentration. We believe this is due to the same shielding hypothesized for the F-type star. For the K star scenario, our preliminary analysis used a K2.5V star; once our analysis is complete for a later K dwarf (K6V) we anticipate seeing more atmospheric CH4 for that star. 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). The results of the simulations, in addition to being useful for biosignature/false-positive discrimination, are also useful for “decision trees” for future telescope mission concepts. The goal of such decision trees is to conduct as few “observations” as possible, to 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 forward in understanding the probability of detecting the presence of life on nearby exoplanets from remote observations.


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