Abstract
We examine the epistemological foundations of a leading technique in the search for evidence of life on exosolar planets. Specifically, we consider the “transit method” for spectroscopic analysis of exoplanet atmospheres, and the practice of treating anomalous chemical compositions of the atmospheres of exosolar planets as indicators of the potential presence of life. We propose a methodology for ranking the anomalousness of atmospheres that uses the mathematical apparatus of support vector machines, and which aims to be agnostic with respect to the particular chemical biosignatures of life. We argue that our approach is justified by an appeal to the “hinge” model of epistemic justification first proposed by as reported by Wittgenstein (On certainty, Blackwell, Oxford, 1969). We then compare our approach to previous work due to Walker et al. (Astrobiology 18(6):779–824, 2018) and Cleland (Astrobiology 19(6):722–729, 2019a; Does ‘life’ have a definition?, Cambridge University Press, New York, 2019b).
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Notes
In taking this aspect of Seager and Bains’ approach as a scientific basis for a metabolism-based epistemology of life detection, we follow Knuuttila and Loettgers (2017).
Having said this, some of our assumptions could, with more data on the chemical compositions of exosolar atmospheres, undergo a “sanity check” by ensuring that Earth is classified as having an anomalous chemical composition in its atmosphere, thereby identifying it as a potential location for life.
That said, we do take the following quote from On Certainty to indicate that the hinge epistemological method is meant to be relevant to the assumptions used in science: “All testing, all confirmation and disconfirmation of a hypothesis takes place already within a system. And this system is not a more or less arbitrary and doubtful point of departure for all our arguments: no, it belongs to the essence of what we call an argument. The system is not so much the point of departure, as the element in which arguments have their life” (p. 107).
References
Achinstein P (2018) Speculation: within and about science. Oxford University Press, Oxford
Anet FA (2004) The place of metabolism in the origin of life. Curr opini chem Biol 8(6):654–659
Banerjee A, Burlina P, Diehl C (2006) A support vector method for anomaly detection in hyperspectral imagery. IEEE Trans Geosci Remote Sens 44(8):2282–2291
Bich L, Green S (2018) Is defining life pointless? operational definitions at the frontiers of biology. Synthese 195(9):3919–3946
Burrows AS (2014) Spectra as windows into exoplanet atmospheres. Proc Natl Acad Sci 111(35):12601–12609
Chela-Flores J (2011) The science of astrobiology: a personal view on learning to read the book of life, vol 20. Springer Science & Business Media, New York
Cleland CE (2019) Moving beyond definitions in the search for extraterrestrial life. Astrobiology 19(6):722–729
Cleland CE (2019) The quest for a universal theory of life: searching for life as we don’t know it, vol 11. Cambridge University Press, Cambridge
Cleland CE, Chyba C (2002) Defining ‘life’. Orig Life Evol Biosph 32(4):387–393
Cleland CE, Chyba C (2007) Does ‘life’ have a definition? In The nature of life: classical and contemporary perspectives from philosophy and science. Cambridge University Press, New York
Currie A (2018) Rock, bone, and ruin: An optimist’s guide to the historical sciences. MIT Press, Cambridge
Currie A (2019) Epistemic optimism, speculation, and the historical sciences. Philos Theor Pract Biol 11:7
Currie A (2021) Science & speculation. Erkenntnis. https://doi.org/10.1007/s10670-020-00370-w
Currie A, Sterelny K (2017) In defence of story-telling. Stud Hist Philos Sci Part A 62:14–21
Dick SJ (2012) Critical issues in the history, philosophy, and sociology of astrobiology. Astrobiology 12(10):906–927
Dyson F (1999) Origins of life. Cambridge University Press, Cambridge
Fry I (2000) The emergence of life on earth: a historical and scientific overview. Rutgers University Press, New Brunswick
Ghafoori Z, Erfani SM, Rajasegarar S, Bezdek JC, Karunasekera S, Leckie C (2018) Efficient unsupervised parameter estimation for one-class support vector machines. IEEE Trans Neural Netw Learn Syst 29(10):5057–5070
Hörst SM (2017) Titan’s atmosphere and climate. J Geophys Res: Planets 122(3):432–482
Hörst SM, Vuitton V, Yelle RV (2008) Origin of oxygen species in titan’s atmosphere. J Geophys Res: Planets 113:E10
Kauffmann S (1993) The origins of order. Oxford University Press, Oxford
Kempes CP, Krakauer DC (2021) The multiple paths to multiple life. J Mol Evol 89(7):415–426
Kishimoto A, Buesser B, Botea A (2018) Ai meets chemistry. In: Thirty-Second AAAI Conference on Artificial Intelligence
Knuuttila T, Loettgers A (2017) What are definitions of life good for? transdisciplinary and other definitions in astrobiology. Biol Philos 32(6):1185–1203
Kolmogorov AN (1933) Foundations of the theory of probability: second, English. Courier Dover Publications, New York
Laudan L (1981) A confutation of scientific realism. Philos Sci 48:19–49
Léger A, Pirre M, Marceau F (1993) Search for primitive life on a distant planet: relevance of 02 and 03 detections. Astron Astrophys 277:309
Lv K-P, Norman L, Li Y-L (2017) Oxygen-free biochemistry: the putative CHN foundation for exotic life in a hydrocarbon world? Astrobiology 17(11):1173–1181
Machery E (2012) Why i stopped worrying about the definition of life... and why you should as well. Synthese 185(1):145–164
Narita N, Enomoto T, Masaoka S, Kusakabe N (2015) Titania may produce abiotic oxygen atmospheres on habitable exoplanets. Sci Rep 5:13977
Patel BH, Percivalle C, Ritson DJ, Duffy CD, Sutherland JD (2015) Common origins of RNA, protein and lipid precursors in a cyanosulfidic protometabolism. Nat Chem 7(4):301
Pritchard D (2017) Wittgenstein on hinge commitments and radical scepticism in on certainty. A companion to Wittgenstein. Blackwell, Oxford, pp 563–575
Ralser M (2018) An appeal to magic? the discovery of a non-enzymatic metabolism and its role in the origins of life. Biochem J 475(16):2577–2592
Reichenbach H (1938) Experience and prediction: an analysis of the foundations and the structure of knowledge. University of Chicago Press, Chicago
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
Schrödinger E (1944) What is life? Cambridge University Press, Cambridge
Seager S (2014) The future of spectroscopic life detection on exoplanets. Proc Natl Acad Sci 111(35):12634–12640
Seager S, Bains W (2015) The search for signs of life on exoplanets at the interface of chemistry and planetary science. Sci Adv 1(2):e1500047
Seager S, Bains W, Petkowski J (2016) Toward a list of molecules as potential biosignature gases for the search for life on exoplanets and applications to terrestrial biochemistry. Astrobiology 16(6):465–485
Spiegel DS, Turner EL (2012) Bayesian analysis of the astrobiological implications of life’s early emergence on earth. Proc Natl Acad Sci 109(2):395–400
Swain M (2010a) Exoplanet spectroscopy: a bright present, a brilliant future. In: EGU General Assembly Conference Abstracts, vol 12, p 7631
Swain MR (2010b) Finesse-a new mission concept for exoplanet spectroscopy. In: Bulletin of the American Astronomical Society, vol 42, p 1064
Vance DB, Jacobs JA (2005) Water, bacteria, life on mars, and microbial diversity. Water Encycl 4:746–748
Walker SI, Bains W, Cronin L, DasSarma S, Danielache S, Domagal-Goldman S, Kacar B, Kiang NY, Lenardic A, Reinhard CT et al (2018) Exoplanet biosignatures: future directions. Astrobiology 18(6):779–824
Wittgenstein L (1969) On certainty, vol 174. Blackwell, Oxford
Wright C (2004) Warrant for nothing (and foundations for free)? Aristotelian Society Supplementary Volume, vol 78. Wiley Online Library, New York, pp 167–212
Acknowledgements
We are very grateful to Artemy Kolchinsky for detailed feedback on an earlier draft of this paper, to David Wolpert and Natalie Grefenstette for helpful discussions, and to audiences at Philosophy of Biology at the Mountains hosted by the University of Utah, an online audience at the meeting of the European Philosophy of Science Association, and the Uncovering the Laws of Life Workshop in Grindavik, Iceland. Christopher Kempes’ work on this project was supported by the National Aeronautics and Space Administration (Grant No. 80NSSC18K1140) and by Toby Shannan and Charities Aid Foundation of Canada (CAF).
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Appendices
Appendix A
Our measure of anomalousness is defined precisely as follows. Let \(\vec {w}_{i}\) be a vector with n entries, where each entry \(w_{ij}\) is the abundance of gas j from some subset of Seager et al.’s list in planet i’s atmosphere (namely, some subset of the set of gases that can be measured in a given context). Let the set of observed composition vectors be \(\mathbf {W}\). Our proposed anomalousness measure draws on support vector machine (SVM) based approaches to anomaly detection in high-dimensional space, as proposed by Banerjee et al. (2006). Specifically, we propose to measure the anomalousness \(\mathcal {A}(\vec {w}_{i},\mathbf {W})\) of the atmospheric composition of a given planet \(W_{i}\), in the context of a set of planets \(\mathbf {W}\) with cardinality m, using the following equation:
See Banerjee et al. (2006) for a detailed derivation of this particular measure of anomalousness. Each \(\alpha _{j}\) and \(\alpha _{k}\) is a positive, real-number entry in a vector \(\vec {\alpha }\) in which each entry is a specific weighting parameter for each planet. The norm \(||\vec {w}_{i}-\vec {w}_{j}||\) denotes the Euclidean distance between the vectors \(\vec {w}_{i}\) and \(\vec {w}_{j}\). The scale parameter \(\sigma\) sets the shape of the distribution of the anomalousness of the planets in the dataset. Thus, if \(\vec {w}_{i}\) is an abnormally large distance from all of the other data vectors in the set, then the first term of the equation will be larger and the second term will be smaller, and so \(\mathcal {A}(\vec {w}_{i},\mathbf {W})\) will be larger, all else being equal. This justifies the claim that higher the value of \(\mathcal {A}(\vec {w}_{i},\mathbf {W})\), the greater the anomalousness of the atmospheric composition of the atmosphere of the planet \(W_{i}\).
The scale parameter \(\sigma\) and the weight vector \(\vec {\alpha }\) can be estimated from the available data. To estimate \(\sigma\), we use a technique from Ghafoori et al. (2018). Let \(\delta _{min}\) be the minimal Euclidean distance of any vector \(\vec {w_{i}}\) from its nearest neighbor in the data set \(\mathbf {W}\). Let \(\delta _{avg}\) be the average Euclidean distance of all vectors in \(\mathbf {W}\) from their nearest neighbor, excluding the vector that is \(\delta _{min}\) from its nearest neighbor. The scale parameter \(\sigma\) is estimated as follows:
This method for estimating \(\sigma\) has proven successful at detecting anomalies in a series of empirical tests, using standard machine-learning data sets (Ghafoori et al., 2018, pp. 5064-5069). Following Schölkopf et al. (2001), we can set the weight vector \(\vec {\alpha }\) by solving the following optimization problem:
This ensures that \(\mathcal {A}(\vec {w}_{i},\mathbf {W})\) assigns as high anomalousness as possible to each planet \(W_{i}\).
Appendix B
To demonstrate the potential applicability of our proposed anomaly-detection method, we tested our method on simulated data. The simulation proceeded in the following steps:
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1.
Generate 90 gas abundance vectors by repeatedly sampling from a Dirichlet distribution \(\text {Dir}(1000,\vec {u})\), where \(\vec {u}=[u_{1},\dots ,u_{1000}]\) is a concentration parameter such that \(u_{i}=10\) for \(i\le 100\) and \(u_{i}=1\) for \(i>100\). This ensures that abundance vectors sampled from this distribution are expected to have abundances concentrated near the first 100 entries. This is meant to simulate observations from a typical, lifeless planet, on which certain gases are much more likely to be common than others. Call this sample of abundance vectors \(\mathbf {W_{A}}\).
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2.
Generate 10 gas abundance vectors by repeatedly sampling from a Dirichlet distribution \(\text {Dir}(1000,\vec {v})\), where \(\vec {v}=[v_{1},\dots ,v_{1000}]\) is a concentration parameter such that \(v_{i}=x\) for \(i\le 100\) and \(v_{i}=1\) for \(i>100\). For higher values of x, abundance vectors sampled from this distribution are expected to have abundances concentrated near the first 100 entries. This is meant to simulate observations from a typical, lifeless planet, on which certain gases are much more likely to be common than others. For lower values of x, all possible abundance vectors become increasingly likely. This is meant to simulate observations from planets that are possible hosts of life, where many more combinations of gas abundances are assumed to be possible. Call this sample of abundance vectors \(\mathbf {W_{B}}\).
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3.
Form the full data set \(\mathbf {W}=\mathbf {W_{A}}\cup \mathbf {W_{B}}\).
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Compute the parameters \(\sigma\) and \(\vec {\alpha }\) of the anomalousness measure for the data set \(\mathbf {W}\).
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5.
Calculate the anomalousness measure \(\mathcal {A}(\vec {w},\mathbf {W})\) for each element of \(\mathbf {W}\).
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6.
Identify the ten most anomalous abundance vectors in \(\mathbf {W}\).
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7.
Record as y the proportion of the ten most anomalous vectors that are elements of \(\mathbf {W_{B}}\).
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8.
Repeat steps 1-7 for all possible values of x in the interval (1, 10), in increments of .2.
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9.
Repeat steps 1-8 fifty times, for a total of 460 data points.
Recall that lower values of the parameter x, which determines the concentration parameter for the distribution from which elements of \(\mathbf {W_{B}}\) are sampled, are meant to simulate cases in which the elements of \(\mathbf {W_{B}}\) represent planets that are possible hosts of life. Thus, we expect that as x increases, the proportion of abundance vectors with top-ten anomalousness in \(\mathbf {W}\) that are also elements of \(\mathbf {W_{B}}\) should decrease.
Indeed, this is what we observe in the results of our simulation. Figure 2 shows the proportion of top-ten anomalies from the set \(\mathbf {W}\) that are also elements of the set \(\mathbf {W_{B}}\), for each value of x in each simulation. For values of x lower than or equal to 6, the proportion of top-ten anomalous vectors that are elements of \(\mathbf {W_{B}}\) is tightly clustered around 1. For values of x greater than 6, this same proportion is declining linearly in x. We take this to be positive evidence for the in-principle utility of our measure for detecting the kinds of anomalies that we are interested in. It should be noted that while there is a fairly sharp transition in correctly identifying \(W_B\), knowing the value of x (or the analogous parameter(s) in a given setting) at which this transition occurs in an applied setting will require careful thought. As pointed out above, correct identification depends on the degree of anomalousness and thus, such methods are expected to fail when life is not very anomalous. Code for this simulation is available at https://github.com/davidbkinney/astrobiologyappendixB.
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Kinney, D., Kempes, C. Epistemology and anomaly detection in astrobiology. Biol Philos 37, 22 (2022). https://doi.org/10.1007/s10539-022-09859-w
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DOI: https://doi.org/10.1007/s10539-022-09859-w