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arXiv 0810.4329astro-ph
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在不引入任何额外偏差的情况下减小Ia型超新星的引力透镜散射

Reducing the gravitational lensing scatter of Type Ia supernovae without introducing any extra bias

Jakob Jonsson, Edvard Mortsell, Jesper Sollerman

摘要:

由于引力透镜的放大和缩小作用,将对Ia型超新星的亮度散射产生贡献。本文的目的是调查的可能性,以减少这种散射校正个别超新星Ia星系的观测前景,而不引入任何额外的偏见。我们模拟了大量的超新星Ia星系的视线。对于每个视线,计算真实放大因子及其估计。估计的放大系数对应于观察者从SNLS等调查中推断的结果。使用模拟数据,我们调查的可能性,以估计放大率的个别超新星有足够的精度,能够纠正其亮度的引力透镜与可忽略不计的偏差。我们的模拟表明,由单个超新星Ia的引力透镜校正所产生的偏差对于当前和下一代的调查是可以忽略的,并且来自透镜的散射可以减少大约2倍。对于0.13等的本征色散,Ia超新星星等的总散射可以减少4%。对于0.09等的本征色散,这对于未来的调查可能不是不切实际的,总散射可以减少6%。这将使从超新星数据得出的宇宙学参数的误差减少4- 8%。因此,校正透镜效应的前景非常好。

英文摘要:

Magnification and de-magnification due to gravitational lensing will contribute to the brightness scatter of Type Ia supernovae (SNe Ia). The purpose of this paper is to investigate the possibility to decrease this scatter by correcting individual SNe Ia using observations of galaxies in the foreground, without introducing any extra bias. We simulate a large number of SN Ia lines of sight populated by galaxies. For each line of sight the true magnification factor and an estimate thereof are calculated. The estimated magnification factor corresponds to what an observer would infer from a survey like SNLS. Using the simulated data we investigate the possibility to estimate the magnification of individual supernovae with enough precision to be able to correct their brightness for gravitational lensing with negligible bias. Our simulations show that the bias arising from gravitational lensing corrections of individual SNe Ia is negligible for current and next generation surveys and that the scatter from lensing can be reduced by approximately a factor 2. The total scatter in the SN Ia magnitudes could be reduced by 4% for an intrinsic dispersion of 0.13 mag. For an intrinsic dispersion of 0.09 mag, which might not be to unrealistic for future surveys, the total scatter could be reduced by 6%. This will reduce the errors on cosmological parameters derived from supernova data by 4-8%. The prospect of correcting for lensing is thus very good.

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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