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arXiv 0909.2692astro-ph.CO
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宇宙学与Ia型超新星的光度测量

Cosmology with Photometric Surveys of Type Ia Supernovae

Yan Gong, Asantha Cooray, Xuelei Chen

摘要:

我们讨论了在何种程度上光度测量可以单独用于识别Ia型超新星(SNIa),并确定宇宙学研究的红移和其他参数的兴趣。我们拟合的光变曲线数据的类型,预计从一个调查,如计划与大型综合巡天望远镜(LSST),并删除污染的核心坍缩超新星SNIa样本。我们根据现有的设计参数为每个LSST滤波器生成1000个SNIa模拟通量数据,然后使用马尔可夫链蒙特卡罗(MCMC)分析来拟合SNIa的红移、视星等、拉伸因子和相位。我们发现,当真实SNe红移低于0.5时,模型拟合工作得足够好,而在$z < 0.2$,光度数据的准确性几乎与光谱测量相同的样品。本文讨论了SNIa数据集上Ib/c型超新星(SNIb/c)和II型超新星(SNII)的污染问题。我们发现,它是很容易区分的SNII通过大的$χ^2$不匹配时,拟合光度数据与Ia光变曲线。SNIb/c的情况并非如此。我们实现了一个统计方法的基础上的贝叶斯估计,以统计减少污染从SNIb/c的宇宙学参数测量从整个SNe样本。所提出的统计方法还评估了SNIa在总SNe数据集中的分数,这为确定污染程度提供了有价值的指导。

英文摘要:

We discuss the extent to which photometric measurements alone can be used to identify Type Ia supernovae (SNIa) and to determine redshift and other parameters of interest for cosmological studies. We fit the light curve data of the type expected from a survey such as the one planned with Large Synoptic Survey Telescope (LSST) and also to remove the contamination from the core-collapse supernovae to SNIa samples. We generate 1000 SNIa mock flux data for each of the LSST filters based on existing design parameters, then use a Markov Chain Monte-Carlo (MCMC) analysis to fit for the redshift, apparent magnitude, stretch factor and the phase of the SNIa. We find that the model fitting works adequately well when the true SNe redshift is below 0.5, while at $z < 0.2$ the accuracy of the photometric data is almost comparable with spectroscopic measurements of the same sample. We discuss the contamination of Type Ib/c (SNIb/c) and Type II supernova (SNII) on the SNIa data set. We find it is easy to distinguish the SNII through the large $χ^2$ mismatch when fitting to photometric data with Ia light curves. This is not the case for SNIb/c. We implement a statistical method based on the Bayesian estimation in order to statistically reduce the contamination from SNIb/c for cosmological parameter measurements from the whole SNe sample. The proposed statistical method also evaluate the fraction of the SNIa in the total SNe data set, which provides a valuable guide to establish the degree of contamination.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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