Exoplanet Biosignatures: Future Directions

We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets—Biosignatures—Life detection—Bayesian analysis. Astrobiology 18, 779–824. 1. Introduction 2. Setting the Stage: What Is Life? What Is a Biosignature? 3. Detecting Unknown Biology on Unknown Worlds: A Bayesian Framework 3.1. Habitability in the Bayesian framework for biosignatures 4. P(data|abiotic) 4.1. Stellar environment 4.2. Climate and geophysics 4.2.1. Coupled tectonic–climate models 4.2.2. Community GCM projects for generating ensemble statistics for P(data|abiotic) and P(data|life) 4.3. Geochemical environment 4.3.1. Anticipating the unexpected: statistical approaches to characterizing atmospheres of non-Earth-like worlds 5. P(data|life) 5.1. Black-box approaches to living processes 5.1.1. Type classification of Seager et al. (2013a) 5.1.1.1. Energy capture (type I) 5.1.1.2. Biomass capture (type II) 5.1.1.3. Other uses (type III) 5.1.1.4. Products of modification of gases (type IV) 5.1.2. Alternatives for type classification 5.1.2.1. Type I, energy capture 5.1.2.2. Type II, biomass capture 5.1.2.3. Type III, “other uses” 5.1.2.4. Type IV 5.1.3. When is it appropriate to deconstruct a black box? 5.2. Life as improbable chemistry 5.3. Life as an evolutionary process 5.3.1. Life as a coevolution with its planet: Earth as an example 5.3.2. Calculating conditional probabilities in biological evolution from past biogeochemical states 5.4. Insights from universal biology 5.4.1. Network biosignatures 5.4.2. Universal scaling laws, applicable to other worlds? 6. P(life) 6.1. P(emerge): constraining the probability of the origins of life 6.2. Biological innovations and the conditional probabilities for living processes 7. A Bayesian Framework Example: Detecting Atmospheric Oxygen 8. Tuning Search Strategies Based on the Bayesian Framework 9.