Cited by 225Space Science and Extraterrestrial LifeStellar, planetary, and galactic studiesPlanetary Science and Exploration

The DOI matches its Crossref record; 263 of the 263 references it cites were found in a registry or an index; none of the 200 sources its claims cite is checked for support yet.

Exoplanet Biosignatures: A Framework for Their Assessment

Open access
Catling, David C., Krissansen-Totton, Joshua, Kiang, Nancy Y., Crisp, David, Robinson, Tyler D., DasSarma, Shiladitya, Rushby, Andrew J., Del Genio, Anthony, Bains, William, Domagal-Goldman, Shawn·2018·Astrobiology

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.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper proposes a Bayesian framework for assessing exoplanet biosignatures, combining prior knowledge about habitability with likelihoods of spectral or photometric data under scenarios with and without life. It outlines four observational/analytical components and maps posterior probabilities of life to five qualitative confidence levels.

Key findings

  • The framework’s central quantity is \(P(\text{life} \mid \text{data, context})\), calculated from priors \(P(\text{life} \mid C)\) and likelihoods \(P(D \mid C, \text{life})\) and \(P(D \mid C, \text{no life})\), where the latter includes abiotic false positives.
  • In an illustrative example with prior \(P(\text{life} \mid C)=0.5\), \(P(D \mid C,\text{life})=0.80\), and \(P(D \mid C,\text{no life})=0.25\), the posterior is \(0.76 \equiv 76\%\).
  • The authors propose five confidence levels for life detection: Level 1 “very likely inhabited” 90–100%, Level 2 “likely inhabited” 66–100%, Level 3 “about as likely as not inhabited” 33–66%, Level 4 “likely uninhabited” 0–33%, and Level 5 “very likely uninhabited” 0–10%.
  • The four required components are: (1) stellar and external exoplanetary system properties, (2) internal exoplanet atmosphere/climate/surface properties, (3) search for potential biosignatures in data, and (4) exclusion of false positives.

Methods

  • Conceptual and statistical framework paper, not an empirical exoplanet observation study; it develops Bayes’ theorem for biosignature assessment and discusses required observations and models.
  • It advocates using biogeochemical “Exo-Earth System” models to simulate potential biosignatures in spectra or photometry and to estimate Bayesian likelihoods with and without life.
  • It reviews desirable stellar, planetary, atmospheric, and surface parameters; candidate gaseous and surface biosignatures; and false-positive scenarios, with illustrative examples rather than new data analysis.

Limitations

  • The paper states that current knowledge of priors such as \(P(\text{life} \mid \text{Earth-like})\) is highly uncertain, ranging from life being very common to very rare, and that extraterrestrial life has not yet been detected.
  • It states that many false-positive scenarios and surface biosignature assessments remain unresolved; for example, “a quantitative framework for evaluating surface biosignatures remains unclear,” and the confidence-level examples are “purely illustrative.”
  • Evident from the text: the framework is not tested on real exoplanet data, relies on future models and observations, and its posterior probabilities cannot yet be quantified for specific exoplanets without detailed calculations.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1089/ast.2017.1737

Cites

263 of 263 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

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. 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.CC BY-NC · PubMed Central
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.
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.CC BY-NC · PubMed Central

Verification

Claims the paper makes

116 Not checked yet · 104 Own interpretation · 46 No basis given

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Show all 126 claims

  • Probably not a substantial claim. 98
  • It may state two separate claims as one. 68
140 of 140
  • The paper relies on this without support.

    Life produces gases and other substances, such as pigments, that can have distinct spectral or photometric signatures.

    “Life produces gases and other substances, such as pigments, which can have distinct spectral or photometric signatures.”
  • The authors state this as an assumption.

    For the framework, the presence of life is treated as a binary variable: a remotely detectable surface biosphere is either present or absent.

    “We take the presence of life to mean the existence of a surface biosphere that is remotely detectable, which we treat as a binary variable, that is, either an exoplanet biosphere is present or it is not.”
    “Because we assume that a biosphere is either present on an exoplanet or absent, it follows that these two are priors are related by”
  • The authors state this as an assumption.

    P(life|O-stars) is negligible.

    “Most people would also consider it reasonable to assume that P(life | O-stars) is negligible because O-stars have main sequence lifetimes that range from less than one to a few million years (Weidner and Vink, 2010).”
  • The authors state this as an assumption.

    In the absence of other information, a prior of P(life | Earth-like) = 0.5 is a practical starting point for an Earth-like planet in the HZ of a Sun-like star.

    “in the absence of any other information, if we found an Earth-like planet in the HZ of a Sun-like star, a starting point—based on empirical data of our current solar system—might be to assume a prior of P(life | Earth-like) = 0.5”
  • The paper relies on this without support.

    Earth's biosphere and its biosignatures are suitable analogs for interpreting potential exoplanet biosignatures.

    “Most currently accepted biosignatures consist of spectral evidence of a biogenic gas molecule.”
  • The paper relies on this without support.

    The Bayesian framework with priors and likelihoods can be meaningfully applied to exoplanet biosignature assessment.

    “Environmental information could be used to improve estimates of data likelihoods or alter the priors shown in Figure 1, whereas new biosignature data would add to the “data” box of Figure 1.”
  • The paper relies on this without support.

    Future exoplanet observations can achieve sufficient spectral resolution and signal-to-noise to detect and quantify biosignature gases and surface features.

    “With data of sufficient fidelity, a Bayesian fit would allow for quantitative constraints on possible gas fluxes that are needed to support a potential biosignature gas.”
  • The paper relies on this without support.

    The geological process of serpentinization produces H2 when liquid H2O oxidizes iron in rocks.

    “The geological process of serpentinization produces H2 when liquid H2O oxidizes iron in rocks.”
  • The paper relies on this without support.

    More than 90% of CH4 on Earth has a direct biogenic source or comes indirectly from biology through thermal breakdown of old organic matter.

    “More than 90% of CH4 on Earth has a direct biogenic source or comes indirectly from biology through thermal breakdown of old organic matter”
9 of 9

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Citations

Cites

Research works printed but not linked: 25

  1. Beichman C.A., 2007, Protostars and Planets V, 915
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  9. Lewis G.N., 1923, Thermodynamics and the Free energy of Chemical Substances
  10. Lubin P., 2016, J Br Interplanet Soc, 69, 40
  11. Mackenzie F.T., 2006, Carbon in the Geobiosphere: Earth's Outer Shell
  12. Monod J., 1971, Chance and Necessity: An Essay on the Natural Philosophy of Modern Biology
  13. Owen T.C., 1964, Commun Lunar Planet Lab, 32, 113
  14. Parenteau M.N., 2015, Astrobiology Science Conference
  15. Qiu J., 2003, J Geophys Res Atmos, 108
  16. Sagan C., 1994, Pale Blue Dot: A Vision of the Human Future in Space
  17. Shklovskii I.S., 1966, Intelligent Life in the Universe
  18. Slonczewski J.L., 2010, Microbe, 5, 110
  19. Stocker T.F., 2013, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
  20. Stone J.V., 2013, Bayes' Rule: A Tutorial Introduction to Bayesian Analysis
  21. Twomey S., 1996, Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements
  22. Wang J., 2015, Astron J, 149
  23. Williams D.M., 1996, Circumstellar Habitable Zones, 43
  24. Wozniak B., 2007, Light Absorption in Sea Water
  25. Zahnle K., 2010, CSH Perspect Biol, 2, a004895

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Metadata

Authors
Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., Crisp, D., Robinson, T. D., DasSarma, S., Rushby, A. J., Del Genio, A., Bains, W., & Domagal-Goldman, S.
Affiliations
  • University of Washington (US)
  • Astrobiology Program, Department of Earth and Space Sciences, University of Washington, Seattle, Washington.
  • Goddard Institute for Space Studies (US)
  • California Institute of Technology (US)
  • University of California, Santa Cruz (US)
  • University of Maryland, Baltimore (US)
  • Ames Research Center (US)
  • NASA Goddard Institute for Space Studies, New York, New York.
  • Massachusetts Institute of Technology (US)
  • Goddard Space Flight Center (US)
Corresponding author
Catling, David C.
Venue
Astrobiology
Published
2018-06
Volume
18
Issue
6
Pages
709-738
ISSN
  • 1531-1074 (ISSN-L)
  • 1557-8070
OpenAlex
W2949593113
Cited by
225
References
258
Citations by year
2026: 17 · 2025: 30 · 2024: 21 · 2023: 30 · 2022: 29 · 2021: 28 · 2020: 16 · 2019: 27 · 2018: 25
FWCI
12.86
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Astrobiology (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Aeronautics and Space Administration (NNX15AR63H, NNX15AM07G, Exobiology grant NNX15AM07G)
  • Universities Space Research Association
  • University of Oxford
  • NASA Astrobiology Institute (NNX15AM07G)
  • Ames Research Center
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Space Science and Extraterrestrial Life
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
  • Physical Sciences › Earth and Planetary Sciences › Space and Planetary Science › Planetary Science and Exploration
Keywords
exoplanet biosignatures, habitability, false positives, Drake equation, exoplanet characterization, planetary climate
MeSH terms
Origin of Life, Bayes Theorem, Extraterrestrial Environment, Planets, Exobiology

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