Compact and Physically Interpretable Feature Models for Photometric Type Ia Supernova Classification

TL;DR

This paper develops a compact, physically interpretable feature model for classifying Type Ia supernovae from photometric data, achieving high accuracy and revealing the key physical descriptors needed for reliable classification.

Contribution

It introduces a reduced, interpretable feature set derived from light curves that maintains high classification performance, simplifying survey design and machine learning transparency.

Findings

Achieved an F1-score of ~0.844 and PR-AUC of ~0.928 with the compact model.

Temporal evolution features dominate classification performance.

A core set of ~10 physical features retains nearly full model performance.

Abstract

Photometric classification of Type Ia supernovae is essential for modern time-domain surveys, where spectroscopic confirmation is not always feasible for the full transient sample. In this work, we investigate a compact and physically interpretable feature representation derived from multi-band light curves and evaluate its performance using gradient-boosted decision trees on the Supernova Photometric Classification Challenge (SPCC) dataset. Starting from a reduced 16-feature model, we perform a systematic feature ablation study to determine which physical descriptors contribute most strongly to classification performance. The final compact model achieves an F1-score of approximately 0.844 and a precision--recall area under the curve (PR-AUC) of approximately 0.928. The ablation results show that temporal evolution provides the dominant classification signal, while brightness, color, and variability features supply complementary information. A reduced core of approximately ten physically meaningful features retains nearly the full performance of the compact model, indicating that reliable classification does not require large high-dimensional feature spaces. These results show that interpretable feature-based models can capture the essential astrophysical information needed for Type Ia photometric classification, with direct implications for survey cadence, filter coverage, and the design of transparent machine learning pipelines for future time-domain surveys.