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

TL;DR

This paper introduces a complex systems network analysis approach to differentiate biological, abiotic, and anomalous sources of biosignature gases in exoplanet atmospheres, enhancing the reliability of detecting extraterrestrial life.

Contribution

It develops a novel chemical reaction network analysis method combined with Bayesian inference to distinguish biosignature origins in simulated exoplanet atmospheres.

Findings

Network properties effectively distinguish methane sources.

Atmospheric network statistics improve confidence in biosignature interpretation.

Method can rule out life-as-we-know-it as an explanation.

Abstract

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 false positive signals because they can also be produced abiotically, raising a critical need to develop methods to determine whether a gas is produced abiotically or not. To distinguish biological, abiotic and anomalous sources (unidentified as abiotic or biotic) of biosignature gases, we take a complex systems approach implementing 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. We show how network properties can effectively distinguish scenarios where CH4 is produced from methanogenesis and serpentinization. Network analysis also distinguishes modern Earth-like atmospheres with CFC-12 from those without it. Using Bayesian analysis, we demonstrate how atmospheric network 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 hypotheses about biological, abiotic and anomalous atmospheric drivers, and importantly, allows ruling out life-as-we-know-it as a possible explanation. We conclude with a discussion of how further developing statistical inference methods for spectral data that incorporate network properties could significantly strengthen future biosignature detection efforts.