How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification

arXiv:2607.03532 · astro-ph.IM, astro-ph.HE, astro-ph.SR · Submitted 2026-07-03 · Read on arXiv

astro-ph.IM, astro-ph.HE, astro-ph.SR

Submitted: 2026-07-03

Updated: 2026-08-29

Comments: 21 pages, 7 figures

Code: https://github.com/FoxFortino/spec_res

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Core — 8× in this paper · explained in 2 episodes
• Signal-to-noise ratio (SNR) — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• AGN — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Active Galactic Nuclei (AGN) — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Observatory Legacy Survey of Space and Time — 1× in this paper
• Provenance — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Zwicky Transient Facility — 1× in this paper

Sources

• DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
• SuperNova IDentification spectral templates of 70 stripped-envelope core-collapse supernovae
• Cosmology from Type Ia Supernovae
• CCSNscore: A multi-input deep learning tool for classification of core-collapse supernovae using SED-Machine spectra
• Attention Is All You Need
• AppleCiDEr. II. SpectraNet: A Spectroscopic Neural Network Classifier for Transients Demonstrated on ZTF Follow-up Data

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