Velocity-SED Correlations in Type 1a Supernovae

1a 型超新星中的速度-SED 相关性

• 批准号: 2442603
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2442603
• 关键词: Velocity; SED; Correlations; Type; 1a

Observations of known-brightness objects are used to obtain distance measurements, which in turn allow for constraints on the fundamental cosmological parameters that describe our Universe. As we enter a new era of precision cosmology with the advent of 'Big Data' surveys such as PS1, DES and LSST, robust control of systematic uncertainties is paramount in yielding unbiased distance estimates. This project will explore the connection between Type 1a Supernovae (SNe 1a) ejecta velocity and the underlying SNe spectral energy distribution (SED), using a state-of-the-art hierarchical Bayesian model BayeSN, so as to control velocity-dependent correlations, and further refine distance measurements for cosmological analysis.Conditioned on supernova optical-NIR light-curve data, BayeSN marries Bayesian statistics with functional data analysis techniques to infer an intrinsic population flux model on time and wavelength, that is further warped on a supernova-by-supernova basis by functional principal components, dust, and a residual covariance function. Foremostly, we will continue to examine whether velocity measurements of the Silicon-II line at supernova peak brightness serve to discriminate between sub-populations of SNe. Historically, a 'redder-faster' relation has been reported, whereby observations of redder (B-V) optical colours are typically coupled to higher ejecta velocities (v<-11km/s). However, owing to the confounding of dust and intrinsic colour variation, this correlation may be an artefact. By contrast, BayeSN models the effects of intrinsic colour and dust extinction independently, meaning we are primed to discern whether this correlation is legitimate. Gaussian mixture models may be exploited to efficiently explore and identify correlated features. Moving forward, velocity measurements at peak will be included in the BayeSN framework, with the expectation that underlying correlations, if any, will be captured, leading to improved distance estimates. In a similar vein, measurements of host-galaxy mass, distance from galaxy-centre, galaxy reddening etc. can also be implemented. Finally, this work could lead to the implementation of velocity measurements as a function of time, to provide even tighter constraints. Alternatively, BayeSN could be adapted to perform a full cosmological analysis, coupled with measurements from the first rung of the distance ladder, e.g. Cepheid variables, Mira Variables, Tip of the Red Giant Branch stars etc. This work will contribute towards the growing community-wide effort to control systematic uncertainties that may bias cosmological analyses.

对已知亮度物体的观测被用来获得距离测量，这反过来又允许对描述我们宇宙的基本宇宙学参数进行限制。随着PS1、DES和LSST等“大数据”调查的出现，我们进入了精确宇宙学的新时代，对系统不确定性的强大控制对于产生无偏距离估计至关重要。本项目将利用最先进的分层贝叶斯模型BayeSN，探索1a型超新星（SNe 1a）喷射速度与潜在的SNe光谱能量分布（SED）之间的联系，从而控制速度依赖的相关性，并进一步细化宇宙学分析的距离测量。BayeSN以超新星光学-近红外光曲线数据为条件，将贝叶斯统计与功能数据分析技术相结合，推断出时间和波长的内在种群通量模型，并通过功能主成分、尘埃和残差协方差函数在超新星的基础上进一步扭曲。最重要的是，我们将继续研究超新星峰值亮度下硅- ii线的速度测量是否有助于区分SNe的亚群。从历史上看，有一种“红-快”的关系，即观测到的更红（B-V）的光学颜色通常与更高的喷射速度（v<-11km/s）相关联。然而，由于灰尘和固有颜色变化的混淆，这种相关性可能是人为的。相比之下，BayeSN独立模拟了固有颜色和尘埃消失的影响，这意味着我们已经准备好辨别这种相关性是否合理。高斯混合模型可以有效地探索和识别相关特征。今后，BayeSN框架将包括峰值速度测量，并期望捕获潜在的相关性（如果有的话），从而改进距离估计。同样，也可以测量宿主星系的质量、与星系中心的距离、星系变红等。最后，这项工作可能导致实现速度测量作为时间的函数，以提供更严格的约束。或者，BayeSN可以用于执行完整的宇宙学分析，加上距离阶梯的第一梯级的测量，例如造父变星，Mira变星，红巨星分支的尖端恒星等。这项工作将有助于不断增长的社区范围内的努力，以控制系统的不确定性，可能会影响宇宙分析。