Operational range bounding of spectroscopy models with anomaly detection

TL;DR

This paper explores how anomaly detection algorithms, especially Isolation Forests, can define operational boundaries for spectroscopy models, ensuring safer predictions in exoplanetary data analysis.

Contribution

It demonstrates the effectiveness of anomaly detection, particularly using SHAP value projections, in establishing operational bounds for spectroscopy models in space missions.

Findings

Isolation Forests effectively identify potential failure contexts.

SHAP value projections improve boundary accuracy.

Trade-offs between coverage and error are analyzed.

Abstract

Safe operation of machine learning models requires architectures that explicitly delimit their operational ranges. We evaluate the ability of anomaly detection algorithms to provide indicators correlated with degraded model performance. By placing acceptance thresholds over such indicators, hard boundaries are formed that define the model's coverage. As a use case, we consider the extraction of exoplanetary spectra from transit light curves, specifically within the context of ESA's upcoming Ariel mission. Isolation Forests are shown to effectively identify contexts where prediction models are likely to fail. Coverage/error trade-offs are evaluated under conditions of data and concept drift. The best performance is seen when Isolation Forests model projections of the prediction model's explainability SHAP values.