A robust, agnostic molecular biosignature based on machine learning

Abstract

The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We used pyrolysis–gas chromatography coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine-learning methods, which resulted in a model that can identify the biogenicity of both contemporary and ancient geologically processed samples with ~90% accuracy. These machine-learning methods do not rely on precise compound identification: Rather, the relational aspects of chromatographic and mass peaks provide the needed information, which underscores this method’s utility for detecting alien biology.

Planetary Complexity Revealed by the Joint Differential Entropy of Eigencolors

“…Determining the signatures of life is challenging because we have no universal definition of life, we only have the example of life as we find it on Earth (Barge et al 2022;Bartlett & Wong 2020;Chou et al 2021aChou et al , 2021bCleland 2019aCleland , 2019bGuttenberg et al 2021;Johnson et al 2018;Marshall et al 2021). However, recent work has demonstrated that general measures of molecular complexity exhibit strong associations with "biogenicity" and hence could be used as agnostic chemical biosignatures (Cleaves et al 2023;Guttenberg et al 2021;Marshall et al 2021). At the planetary scale, entropy-based measures of transit spectra have also been proposed as a way to quantify the similarity of a given exoplanet atmosphere to that of Earth (Vannah et al 2023).…”