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The near-term capability to characterize terrestrial exoplanet atmospheresmay bring us closer to discovering alien life through atmospheric data.However, remotely detectable candidate biosignature gases are subject topossible false positive signals as they can also be produced abiotically. Todistinguish biological, abiotic and anomalous sources of these atmosphericgases, we take a complex systems approach using chemical reaction networkanalysis of planetary atmospheres. We simulated 30,000 terrestrial atmospheres,organized in two datasets: Archean Earth-like worlds and modern Earth-likeworlds. For Archean Earth-like worlds we study cases where CH4 is producedabiotically via serpentinization, biologically via methanogenesis, or fromanomalous sources. We also simulate modern Earth-like atmospheres with andwithout industrial CFC-12. Network properties like mean degree and averageshortest path length effectively distinguish scenarios where CH4 is producedfrom methanogenesis and serpentinization, with biologically driven networksexhibiting higher connectivity and efficiency. Network analysis alsodistinguishes modern Earth atmospheres with CFC-12 from those without, withindustrially polluted networks showing increased mean degree. Using Bayesiananalysis, we demonstrate how atmospheric network property statistics canprovide stronger confidence for ruling out biological explanations compared togas abundance statistics alone. Our results confirm how a network theoreticapproach allows distinguishing biological, abiotic and anomalous atmosphericdrivers, including ruling out life-as-we-know-it as a possible explanation.Developing statistical inference methods for spectral data that incorporatenetwork properties could significantly strengthen future biosignature detectionefforts.

A Complex Systems Approach to Exoplanet Atmospheric Chemistry: New Prospects for Ruling Out the Possibility of Alien Life-As-We-Know-It