Exoplanet Biosignatures: Future Directions
Year: 2018
Type: review
Abstract: 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. Conclusions Acknowledgments Author Disclosure Statement References Abbreviations Used
Source: Astrobiology
Institutions: Santa Fe Institute, Blue Marble Space Institute of Science, Arizona State University, Massachusetts Institute of Technology, University of Glasgow, University of Maryland, Baltimore, Institute of Marine and Environmental Technology, Tokyo Institute of Technology, Sophia University, Goddard Space Flight Center, NASA Astrobiology Institute, University of Washington, Harvard University, University of Arizona, University of Montana, Goddard Institute for Space Studies, Rice University, University of California, Riverside, Georgia Institute of Technology, National Aeronautics and Space Administration, Alternative Earths Astrobiology Center, National Institute of Aerospace, Hampton University, Universities Space Research Association
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: THE CURRENT PROJECT WILL FOCUS ON KEY ASPECTS OF THE BIOLOGY OF HALORUBRUM LACUSPROFUNDI, AN EXTREMELY HALOPHILIC ARCHAEON ISOLATED FROM DEEP LAKE, A, METABOLIC NETWORKS HAVE SCALE FREE TOPOLOGIES, QUITE DIFFERENT FROM RANDOM NETWORKS AND SUGGESTIVE OF UNDERLYING RULES THAT GOVERN THEIR ASSEMBLY AND, G6842844124
OpenAlex ID: W2616924030