Year: 2026
Type: preprint
Abstract: Millions of supernovae will be discovered with the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). As a result, spectrographs around the world will have to make difficult decisions about which supernova candidates receive spectroscopic follow-ups. This work identifies the minimum spectral resolution, $R_λ = \fracλ{Δλ}$, as a function of signal-to-noise ratio (SNR) at which spectral classification of supernova subtypes becomes impossible. We include supernova types Ia, Ia-91T, Ia-91bg, Iax, Ib, Ic, broad-lined Ic, IIb, IIP, and Ibn in this work. We produce a definition of SNR based on specific lines for each SN subtype that allows us to generate homogeneous datasets at 16 different values of $R_λ$ and 14 different SNR's and we tested the classification performance of a recently developed deep-learning classifier, ABC-SN, on each $R_λ$ and SNR combination. We find that classification of supernova spectra into a refined taxonomy that separates, for example, between different subtypes of stripped envelope supernovae, is possible at low resolution and low SNR with no loss in model performance down to $R_λ = 50$ and $\text{SNR} = 5$. Classification performance is only minimally impacted even as low as $R_λ = 25$. We hope that astronomers using the LSST alert stream, as well as designers of future instruments and observatories, will benefit from knowing what spectral resolution is necessary to classify a supernova for arbitrary \SNR{}.
Source: arXiv (Cornell University)
Institutions: University of Delaware, University of Virginia, NSF NOIRLab
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Every Datapoint Counts: Atmosphere-aided Flare Studies in the Rubin era, Collaborative Research: Unravelling Stripped Supernovae - Synergy between Observations and Modeling, Chance favors the prepared astrophysicists: forcing serendipitous discoveries in the era of big surveys and AI, Collaborative Research: HDR DSC: Delaware and Mid-Atlantic Data Science Corps, G7622352226, NSF-Simons AI Institute for Cosmic Origins
OpenAlex ID: W7167615181