SNID-SAGE: A Modern Framework for Interactive Supernova Classification and Spectral Analysis

TL;DR

SNID-SAGE is a comprehensive, interactive framework for supernova spectral classification that combines spectral analysis, template matching, and confidence estimation, supporting large-scale processing and integration into survey workflows.

Contribution

It introduces an upgradeable spectral template library, an interactive interface, and a robust pipeline for large-scale supernova spectral classification and analysis.

Findings

High accuracy in leave-one-out cross-validation tests.

Effective large-scale classification of 46,000 spectra from WISeREP.

Public availability of the framework and validation results.

Abstract

We present SNID-SAGE (SuperNova IDentification-Spectral Analysis and Guided Exploration), a framework for supernova spectral classification with both a fully interactive graphical interface and a scriptable command-line pipeline for large-scale processing. The pipeline combines deterministic spectral preprocessing, FFT-based cross-correlation against a curated template library, ranking of candidate matches using a composite quality metric, and consolidation of redshift and classification solutions into a single result with associated quality and confidence estimates. SNID-SAGE includes an upgradeable template library (about 6000 spectra), interactive line identification with velocity measurements, and optional natural-language summaries of classification results. We evaluate SNID-SAGE using two complementary tests: (i) leave-one-out cross-validation, in which each template spectrum is matched against the remainder of the library; and (ii) large-scale application to WISeREP spectra with valid coverage across the 4000-7000 A interval, irrespective of spectral type, comprising approximately 46 000 spectra, with redshift validation against known host-galaxy measurements where available. The full validation results and the SNID-SAGE framework are publicly available, supporting integration into spectroscopic survey workflows.