Exoplanet Biosignatures: A Framework for Their Assessment

TL;DR

This paper proposes a Bayesian framework combining models and observations to assess the likelihood of life on exoplanets based on spectral biosignatures, accounting for false positives and environmental context.

Contribution

It introduces a comprehensive, probabilistic framework using biogeochemical models and Bayesian analysis for evaluating exoplanet biosignatures.

Findings

Framework maps probabilities to five confidence levels.

Incorporates false positive mitigation strategies.

Integrates observational data with models for robust assessment.

Abstract

Finding life on exoplanets from telescopic observations is an ultimate goal of exoplanet science. Life produces gases and other substances, such as pigments, which can have distinct spectral or photometric signatures. Whether or not life is found with future data must be expressed with probabilities, requiring a framework of biosignature assessment. We present a framework in which we advocate using biogeochemical "Exo-Earth System" models to simulate potential biosignatures in spectra or photometry. Given actual observations, simulations are used to find the Bayesian likelihoods of those data occurring for scenarios with and without life. The latter includes "false positives" where abiotic sources mimic biosignatures. Prior knowledge of factors influencing planetary inhabitation, including previous observations, is combined with the likelihoods to give the Bayesian posterior probability of life existing on a given exoplanet. Four components of observation and analysis are necessary. 1) Characterization of stellar (e.g., age and spectrum) and exoplanetary system properties, including "external" exoplanet parameters (e.g., mass and radius) to determine an exoplanet's suitability for life. 2) Characterization of "internal" exoplanet parameters (e.g., climate) to evaluate habitability. 3) Assessment of potential biosignatures within the environmental context (components 1-2) and any corroborating evidence. 4) Exclusion of false positives. The resulting posterior Bayesian probabilities of life's existence map to five confidence levels, ranging from "very likely" (90-100%) to "very unlikely" ($\le$10%) inhabited.