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Inferring chemical disequilibrium biosignatures for Proterozoic Earth-like exoplanets

Amber V. YoungDepartment of Astronomy and Planetary Sciences, Northern Arizona University, Flagstaff, AZ, USATyler D. RobinsonLunar and Planetary Laboratory, University of Arizona, Tucson, AZ, USAJoshua Krissansen‐TottonEarth and Space Sciences, University of Washington, Seattle, WA, USAEdward W. SchwietermanDepartment of Earth and Planetary Sciences, University of California Riverside, Riverside, CA, USANicholas F. WoganSpace Science Division, NASA Ames Research Center, Moffett Field, CA, USAM. J. WayNASA Goddard Institute for Space Studies, New York, NY, USALinda E. SohlCenter for Climate Systems Research, Columbia University, New York, NY, USAGiada ArneyNASA Goddard Space Flight Center, Greenbelt, MD, USAChristopher T. ReinhardEarth and Atmospheric Sciences, Georgia Tech, Atlanta, GA, USAMichael LineSchool of Earth and Space Exploration, Arizona State University, Tempe, AZ, USADavid C. CatlingAstrobiology Program, University of Washington, Seattle, WA, USAJames D. WindsorDepartment of Astronomy and Planetary Sciences, Northern Arizona University, Flagstaff, AZ, USA
Nature Astronomyjournal2024en
ABI

Аннотация

Abstract Chemical disequilibrium quantified using the available free energy has previously been proposed as a potential biosignature. However, researchers remotely sensing exoplanet biosignatures have not yet investigated how observational uncertainties impact the ability to infer a life-generated available free energy. We pair an atmospheric retrieval tool to a thermodynamics model to assess the detectability of chemical disequilibrium signatures of Earth-like exoplanets, focusing on the Proterozoic eon when the atmospheric abundances of oxygen–methane disequilibrium pairs may have been relatively high. Retrieval model studies applied across a range of gas abundances revealed that order-of-magnitude constraints on the disequilibrium energy are achieved with simulated reflected-light observations for the high-abundance scenario and high signal-to-noise ratios (50), whereas weak constraints are found for moderate signal-to-noise ratios (20–30) and medium- to low-abundance cases. Furthermore, the disequilibrium-energy constraints are improved by using the modest thermal information encoded in water vapour opacities at optical and near-infrared wavelengths. These results highlight how remotely detecting chemical disequilibrium biosignatures can be a useful and metabolism-agnostic approach to biosignature detection.

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