  The near-term capability to characterize terrestrial exoplanet atmospheres
may bring us closer to discovering alien life through atmospheric data.
However, remotely detectable candidate biosignature gases are subject to
possible false positive signals as they can also be produced abiotically. To
distinguish biological, abiotic and anomalous sources of these atmospheric
gases, we take a complex systems approach using chemical reaction network
analysis of planetary atmospheres. We simulated 30,000 terrestrial atmospheres,
organized in two datasets: Archean Earth-like worlds and modern Earth-like
worlds. For Archean Earth-like worlds we study cases where CH4 is produced
abiotically via serpentinization, biologically via methanogenesis, or from
anomalous sources. We also simulate modern Earth-like atmospheres with and
without industrial CFC-12. Network properties like mean degree and average
shortest path length effectively distinguish scenarios where CH4 is produced
from methanogenesis and serpentinization, with biologically driven networks
exhibiting higher connectivity and efficiency. Network analysis also
distinguishes modern Earth atmospheres with CFC-12 from those without, with
industrially polluted networks showing increased mean degree. Using Bayesian
analysis, we demonstrate how atmospheric network property statistics can
provide stronger confidence for ruling out biological explanations compared to
gas abundance statistics alone. Our results confirm how a network theoretic
approach allows distinguishing biological, abiotic and anomalous atmospheric
drivers, including ruling out life-as-we-know-it as a possible explanation.
Developing statistical inference methods for spectral data that incorporate
network properties could significantly strengthen future biosignature detection
efforts.
