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arXiv 1104.2874astro-ph.CO
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Ia型超新星参数估计:使用现有数据集的两种方法的比较

Type Ia supernova parameter estimation: a comparison of two approaches using current datasets

B. L. Lago, M. O. Calvão, S. E. Jorás, R. R. R. Reis, I. Waga, R. Giostri

摘要:

利用Sloan数字巡天(SDSS)第一年Ia型超新星(SNIa)资料汇编,比较了两种不同的方法(传统的χ^2和完全似然法)在估算星等色散时确定参数约束的方法。我们考虑了宇宙学常数+冷暗物质(ΛCDM)和空间平坦的常数w暗能量+冷暗物质(FwCDM)宇宙学模型,并表明,对于当前数据,采用MLCS 2k2光变曲线拟合器时,最佳拟合值存在微小差异,置信等值线面积存在$\sim$ 30%的差异。对于SALT 2光变曲线拟合器,差异不太显著($\lesssim$的面积差异为13%)。在这两种情况下,似然法给出了更多的限制性约束。我们论证了在处理方差表达式中的参数时,使用完全似然而不是χ^2方法的重要性。

英文摘要:

By using the Sloan Digital Sky Survey (SDSS) first year type Ia supernova (SN Ia) compilation, we compare two different approaches (traditional χ^2 and complete likelihood) to determine parameter constraints when the magnitude dispersion is to be estimated as well. We consider cosmological constant + Cold Dark Matter (ΛCDM) and spatially flat, constant w Dark Energy + Cold Dark Matter (FwCDM) cosmological models and show that, for current data, there is a small difference in the best fit values and $\sim$ 30% difference in confidence contour areas in case the MLCS2k2 light-curve fitter is adopted. For the SALT2 light-curve fitter the differences are less significant ($\lesssim$ 13% difference in areas). In both cases the likelihood approach gives more restrictive constraints. We argue for the importance of using the complete likelihood instead of the χ^2 approach when dealing with parameters in the expression for the variance.

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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