发表机构
University of Delaware; Vera C. Rubin Observatory; University of Virginia; NSF NOIRLab; West Chester University of Pennsylvania（特拉华大学; 维拉·C·鲁宾天文台; 弗吉尼亚大学; 美国国家科学基金会NOIRLab; 宾夕法尼亚州韦斯特切斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究超新星亚型分类所需最低光谱分辨率，通过特定线定义信噪比，生成不同分辨率和信噪比数据集，测试深度学习分类器性能，发现低分辨率低信噪比下仍可分类，为相关人员提供参考。
AI 中文摘要
随着维拉·C·鲁宾天文台时空遗产调查（LSST）将发现数百万颗超新星，世界各地光谱仪需决定对哪些候选超新星进行光谱后续观测。本文确定了超新星亚型光谱分类无法进行时的最低光谱分辨率\(R_{\lambda}=\frac{\lambda}{\Delta \lambda}\)作为信噪比（SNR）的函数。研究多种超新星类型，生成数据集并测试深度学习分类器性能，发现低分辨率低信噪比下仍可分类。
英文摘要
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{}.
Comments21 pages, 7 figures, 5 tables
