Exoplanet Biosignatures: A Framework for Their 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" wherein 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), including corroborating evidence. (4) Exclusion of false positives. We propose that resulting posterior Bayesian probabilities of life's existence map to five confidence levels, ranging from "very likely" (90-100%) to "very unlikely" (<10%) inhabited. Key Words: Bayesian statistics-Biosignatures-Drake equation-Exoplanets-Habitability-Planetary science. Astrobiology 18, 709-738.

FIG. 1.

A Bayesian framework for biosignature assessment. Spectral and/or photometric data that may contain biosignatures are used with models to find likelihoods given the context of the exoplanet, for example, its astrophysical environment. One likelihood is the conditional probability of those data occurring given the context and the hypothesis that the exoplanet has life. Another likelihood is the probability of the data occurring given the context and the hypothesis that the exoplanet has no life. These two likelihoods are weighted by prior knowledge to provide a best-informed (posterior) probability that the exoplanet has life given the spectral and/or photometric data and context. Blue boxes signify data acquisition. Yellow boxes contain conditional probabilities and prior probabilities that are part of Bayes' Theorem (gray oval), which is expressed in Eq. 7 in the text.

FIG. 2.

Four components to assess whether potential biosignatures are best explained by the presence of life. The numbered order and yellow arrows indicate information that an idealized observational strategy would gather sequentially, although in reality the order will likely be different. The blue arrows indicate how practical observation and analysis would require iteration to increase confidence in biosignature acceptance. Alternatively, following the blue arrows could aid in identification of a false positive. These four components, combined with Exo-Earth models (see text), would help to constrain the likelihoods of the biosignature data occurring with and without life: P(data | context, life) and P(data | context, no life), respectively.