Explainable classification of astronomical uncertain time series

TL;DR

This paper introduces an explainable, uncertainty-aware subsequence model for classifying uncertain astronomical time series, achieving performance comparable to state-of-the-art methods while providing interpretability for astrophysical insights.

Contribution

The work presents a novel, explainable model that incorporates data uncertainty directly, improving interpretability and potential for scientific discovery in astrophysics.

Findings

Achieves classification performance comparable to state-of-the-art methods.

Provides explainability by identifying important subsequences in light curves.

Offers a publicly available dataset and code for reproducibility.

Abstract

Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine learning tools are used to help achieving these goals by analyzing transient sources, which are modeled as uncertain time series. Although black-box methods achieve appreciable performance, existing interpretable time series methods failed to obtain acceptable performance for this type of data. Furthermore, data uncertainty is rarely taken into account in these methods. In this work, we propose an uncertaintyaware subsequence based model which achieves a classification comparable to that of state-of-the-art methods. Unlike conformal learning which estimates model uncertainty on predictions, our method takes data uncertainty as additional input. Moreover, our approach is explainable-by-design, giving domain experts the ability to inspect the model and explain its predictions. The explainability of the proposed method has also the potential to inspire new developments in theoretical astrophysics modeling by suggesting important subsequences which depict details of light curve shapes. The dataset, the source code of our experiment, and the results are made available on a public repository.