Paper Detail
Fiorenzo Stoppa, Stephen J. Smartt
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.
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@article{stoppa2026snid,
title = {SNID-SAGE: A Modern Framework for Interactive Supernova Classification and Spectral Analysis},
author = {Fiorenzo Stoppa and Stephen J. Smartt},
year = {2026},
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 solutio},
url = {https://arxiv.org/abs/2603.28741},
keywords = {astro-ph.IM},
eprint = {2603.28741},
archiveprefix = {arXiv},
}
{}