论文标题
表征超新星宇宙学的样品选择
Characterizing the Sample Selection for Supernova Cosmology
论文作者
论文摘要
IA型超新星（SNE IA）用作距离指标，以推断指定宇宙扩展历史的宇宙学参数。参数推断取决于选择分析SN样本的标准。仅对于最简单的选择标准和人口模型才能通过分析计算可能性，否则需要以数值确定，该过程固有地具有错误。可能性的数值错误导致参数推断中的错误。本文列出了玩具示例，其中推断出一组SNE在单个红移中推断出距离模量。参数估计器及其不确定性是使用蒙特卡洛技术计算的。提出了蒙特卡洛实现数量与数值错误之间的关系。该过程可以应用于更现实的模型，并用于确定瞬态分析管道的计算和数据管理要求。
Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.
