Explainable Machine Learning Methods for Ocean Worlds Mass Spectrometry Data: Biosignatures and Environmental Characterization
Description
Future astrobiological and geochemical investigations of ocean worlds (OWs) such as Europa and Enceladus will face challenges that can be addressed through science autonomy. While ML methods are potentially powerful tools for the prediction of geochemistry and biosignatures using IRMS data, many of these models are “black boxes” that reduce trust in predictions, and interpretable ML tools are needed to instill trust. In addition, methods are needed to diagnose false predictions for high-stakes predictions like extraterrestrial life.
Detailed example
One important example is the use of machine learning (ML) models for the prediction of molecular biosignatures (signals of life) from isotope ratio mass spectrometry (IRMS) data on OW orbiters.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
We develop and validate an interpretable ML local variable importance tool called Local Nearest-neighbors Projected Distance Regression (local-NPDR) that improves the explainability of ML models for biosignatures and OW chemistry using real IRMS measurements of volatile CO2 from OW analogue brines and simulated data. We hypothesize that false predictions may be identified when the signs and magnitudes of local and global variable importance scores differ. We add local-NPDR false prediction diagnostics to our interpretable ML algorithms that include global-NPDR feature selection and network visualization of globally-important variables. Together these NPDR-based tools add interpretability to ML models with the ability to detect biosignatures and characterize the environment with respect to pH, CO2 concentration and salt content. Such interpretable ML methods will be important for the implementation of science autonomy for future missions to study plumes of OWs and evaluate their habitability and geochemistry.
Controls / human review
ATO: Not reported; PIA: Not published