Fingerprinting Type I Supernovae: Quantifying Their Diversity and Sharpening Their Utility

光谱中没有氢元素的爆炸恒星被称为1型超新星。它们对天体物理学的许多领域都是至关重要的。最著名的子类是Ia型，它已经产生了关于宇宙加速膨胀的令人兴奋的结果。然而，在精密宇宙学的下一步，需要在非常远的距离识别Ia型超新星，这是困难的，因为它们很容易与其他类型的I型超新星，即Ib/c型超新星混淆。这次调查将研究这些超新星类型之间的观测差异，以消除错误的分类，从而使我们能够更好地了解我们的宇宙在数十亿年前膨胀的早期阶段的加速。Ia型是双星系统中一颗C-O白矮星的热核爆炸的结果，而Ib/c型是大质量恒星的核心坍塌爆炸，它们已经去除了最外层的H层。Ib/c类观测不能用来得出精确的距离测量；然而，因为它们的光谱看起来像1a型超新星，它们可能会污染大型超新星调查。这一奖项是为了对数千颗超新星的光谱和光曲线(随时间发出的光的记录)进行彻底的统计分析，以便区分Ia型超新星和1b和1c型超新星，并将冒名顶替者从调查中剔除。该项目将开发所需的工具，以快速和自动地识别1b/c型超新星并将其从调查中删除。PI将向社区提供这些工具和方法，用于未来使用其他望远镜进行观测，例如大型天气观测望远镜(LSST)，预计每晚将产生数千个新的超新星候选者。这些数据还将被用来测试大质量恒星的核心塌缩爆炸模型，这将提高对恒星演化的了解。PI将培训和指导一名研究生和本科生的研究，她还将与外部组织合作，从科学、技术、工程和数学(STEM)领域代表性不足的群体中招聘一名有前途的高中生。这项工作将通过双管齐下的方法完成：1)利用传统的光谱模板方法和成熟的算法，然后使用主成分分析(PCA)来获得特征谱基，如果以稳健的方式找到，它可以很容易地用于构建平均光谱时间序列模板，并在现有数据中识别任何新的分类方案。此外，PI将通过测试隐藏在SN IC中的He来澄清SN Ib/c的SN光谱分类之间的联系，包括那些与伽马射线暴(GRB)有关的分类，以及它们所谓的恒星前体之间的联系。这将通过将观测到的一大组SNIb/c的光谱和光曲线与两个相互竞争的模型预测的光谱和光曲线进行首次比较来实现。PI将使用最近发表的或正在进行的超过5800次超新星爆炸的光谱和光曲线(如WISeREP和SUKECT)。

Exploding stars that show no Hydrogen in their spectra are called Type 1 supernovovae. They are vital for many areas of astrophysics. The most famous sub-class are the Type Ia, which have yielded exciting results about the accelerating expansion of the universe. However, for the next step in precision cosmology, identifications are needed of Type Ia at very large distances, which is difficult, because they are easily confused with other types of Type I supernovae, namely Type Ib/c. This survey will study the observed differences between these supernova types to eliminate mistaken classifications and thus enable a better understanding of the acceleration of our universe at earlier stages in its expansion, billions of years in the past. While Type Ia are the result of thermonuclear explosions of a C-O white dwarf in a binary system, Type Ib/c are core-collapse explosions from massive stars, which have been removed of their outermost H layer. Type Ib/c observations cannot be used to derive accurate distance measurements; however, because their spectra look like Type 1a supernovae, they can contaminate large supernova surveys. This award is to conduct a thorough statistical analysis of optical spectra and lightcurves (records of light emitted over time) of thousands of supernovae in order to distinguish between Type Ia supernova and Types 1b and 1c and remove the impostors from the surveys. This project will develop the tools needed to quickly and automatically identify and remove Type 1b/c supernovae from surveys. The PI will provide these tools and methods to the community for future observations with other telescopes, such as the Large Synoptic Survey Telescope (LSST), which is expected to yield thousands of new supernova candidates per night. The data will also be used to test models of core-collapse explosions of massive stars, which will improve knowledge of stellar evolution. The PI will train and mentor a graduate student and undergraduate student in research, and she will also collaborate with an external organization to recruit a promising high school student from an underrepresented group in the fields of Science, Technology, Engineering and Math (STEM). The work will be done through a two-pronged approach: 1) utilizing the traditional spectral template approach with well-established algorithms, and then 2) employing Principal Component Analysis (PCA) to derive eigenspectrum bases, which if found in a robust fashion, can be easily used to construct average spectral time-series templates and to identify any new classification schemes within the available data. In addition, the PI will clarify the link between SN spectral classification for SN Ib/c, including those connected with Gamma-Ray Bursts (GRBs), and their purported stellar progenitors by testing for hidden He in SN Ic. This will be done by comparing observed spectra and light curves of a large set of SN Ib/c, for the first time, with those predicted from two competing models. The PI will use spectra and lightcurves of over 5800 supernova explosions that are recently published (such as WISeREP and SUSPECT) or are in-hand.