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arXiv 0812.0370astro-ph
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相似与相似:超新星宇宙学系统学的策略与改进

Like vs. Like: Strategy and Improvements in Supernova Cosmology Systematics

Eric V. Linder

摘要:

在使用Ia型超新星作为标准化距离指标时,可以通过对比观测特征的子集(如超新星)来控制系统的不确定性。本质上,不同红移的超新星揭示了宇宙学,而相同红移的不同超新星揭示了系统学,包括尚未被标准化校正的进化。在这里,我们研究的策略,使用经验定义的子集,以尽量减少宇宙学参数的风险,参数的不确定性和系统偏差的二次和。我们调查的样本内的子集的最佳识别,并讨论了一些问题的观测要求准确测量子集的属性。忽略相似与相似的比较(即只创建一个哈勃图)会导致对暗能量的宇宙学约束被1σ偏置或被1.6倍的因子退化,总漂移为0.02兆。在0.016兆水平(相对差异)的子集识别消除了偏差,并将退化减少到2%。

英文摘要:

Control of systematic uncertainties in the use of Type Ia supernovae as standardized distance indicators can be achieved through contrasting subsets of observationally-characterized, like supernovae. Essentially, like supernovae at different redshifts reveal the cosmology, and differing supernovae at the same redshift reveal systematics, including evolution not already corrected for by the standardization. Here we examine the strategy for use of empirically defined subsets to minimize the cosmological parameter risk, the quadratic sum of the parameter uncertainty and systematic bias. We investigate the optimal recognition of subsets within the sample and discuss some issues of observational requirements on accurately measuring subset properties. Neglecting like vs. like comparison (i.e. creating only a single Hubble diagram) can cause cosmological constraints on dark energy to be biased by 1σor degraded by a factor 1.6 for a total drift of 0.02 mag. Recognition of subsets at the 0.016 mag level (relative differences) erases bias and reduces the degradation to 2%.

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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