Statistical Classification of Life Detection Data: Combinatorial Biosignatures and Mission Evaluation

Imagine a set of measurements-spectral, electrochemical, morphological-returned from a life detection mission. Can the set distinguish between 'indicative of life' and 'not'?

The need for empirical data sufficiency criteria is specific to life detection because of the lack of consensus in the field. However, recent proposed missions include astrobiological instrument packages despite disagreement over interpretation. The recently published Ladder of Life Detection acknowledges a need to quantify how different combinations of biosignatures affect overall confidence.

Machine learning classification techniques could theoretically allow quantitative evaluation of combinations of biosignatures. They have already been utilized to help analyze single-instrument measurements for biosignatures and to determine sample target or analysis priority. However, the need for sufficient training and validation data is a significant challenge.

There have been a number of recently established biogeochemical databases and ontologies that could allow 'agnosticized' terrestrial analogue systems to be generated. This would require removing data specific to Earth biochemistry (presence of ATP, rRNA, etc.) and standardizing values for both biogenic and abiogenic systems across a number of mission-relevant parameters.

An early-stage investigation of this approach has identified a number of data sources and potential feature sets that could be used to implement this approach, as well as a set of algorithms that should be compared against one another. For example, an algorithm trained for Europa or Enceladus might start with a feature list of elemental fractionation, enantiomeric excess, isotope fractionation, chemical species number, synthesis pathway length, mean molecular mass, edge complexity, and standard enthalpy of formation.