为下一代Space Telescope观测扩展超新星光谱模板
Extending Supernova Spectral Templates for Next-Generation Space Telescope Observations
摘要：
超新星（SN）光谱能量分布（SED）的经验模型被广泛用于SN巡天模拟和光度分类。现有的SED模型库具有出色的光学模板，但对紫外（UV）和红外（IR）波长的覆盖范围有限且约束较差。然而，这两种制度对于未来SN调查的设计和操作都是至关重要的，特别是在詹姆斯韦伯太空望远镜（JWST）和宽视场红外巡天望远镜（WFIRST）可以访问的红外波长。我们使用Ia型和核心塌陷（CC）光度光变曲线的采样创建了一个改进的经验SED模板的公共存储库，以扩展Ia型参数化SALT 2模型和一组SN Ib，SN Ic，和SN II SED模板进入UV和近-我们应用这个新的外推SN SED模型库来研究未来的调查如何在UV和IR上区分CC和Ia型SNe波长，并提出了一个开源软件包编写的Python，SNSEDextend，使用户能够生成自己的外推SED。
英文摘要：
Empirical models of supernova (SN) spectral energy distributions (SEDs) are widely used for SN survey simulations and photometric classifications. The existing library of SED models has excellent optical templates but limited, poorly constrained coverage of ultraviolet (UV) and infrared (IR) wavelengths. However, both regimes are critical for the design and operation of future SN surveys, particularly at IR wavelengths that will be accessible with the James Webb Space Telescope (JWST) and the Wide-Field Infrared Survey Telescope (WFIRST). We create a public repository of improved empirical SED templates using a sampling of Type Ia and core-collapse (CC) photometric light curves to extend the Type Ia parameterized SALT2 model and a set of SN Ib, SN Ic, and SN II SED templates into the UV and near-IR. We apply this new repository of extrapolated SN SED models to examine how future surveys can discriminate between CC and Type Ia SNe at UV and IR wavelengths, and present an open-source software package written in Python, SNSEDextend, that enables a user to generate their own extrapolated SEDs.
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