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arXiv 1507.07560astro-ph.CO
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探测Ia型超新星追踪的宇宙加速的各向同性

Probing the isotropy of cosmic acceleration traced by Type Ia supernovae

Behnam Javanmardi, Cristiano Porciani, Pavel Kroupa, Jan Pflamm-Altenburg

摘要:

本文提出了一种方法来检验Ia型超新星(SNe Ia)的星等-红移关系的各向同性,并根据全天观测数据选出最不一致的方向(以信噪比计)。我们的技术占可能的方向变化的SNe Ia的修正,并产生任意角分辨率的最佳拟合宇宙学参数的全天空地图。为了展示其潜力,我们将我们的方法应用于最近的Union2.1编译,构建具有三种不同角分辨率的地图。我们使用蒙特卡罗方法估计的统计意义,我们可以拒绝的零假设,即星等红移关系是各向同性的基础上观察到的最不一致的方向的属性。我们发现,基于纯信号噪声参数,零假设不能被拒绝在任何有意义的置信水平。然而,如果我们还考虑到Union2.1样本中最强的偏差与宇宙微波背景的偶极温度各向异性密切相关,我们发现零假设应该在$95-99$ %置信水平下被拒绝,略微取决于研究的角分辨率。如果这一结果不是由于统计上的侥幸,它可能表明SN数据还没有从所有可能的系统学中清除,甚至指向新的物理学。最后,我们讨论了该领域的未来前景,以实现更大,更统一的数据集,这将大大提高结果的质量,并最佳地利用我们的方法。

英文摘要:

We present a method to test the isotropy of the magnitude-redshift relation of Type Ia Supernovae (SNe Ia) and single out the most discrepant direction (in terms of the signal-to-noise ratio) with respect to the all-sky data. Our technique accounts for possible directional variations of the corrections for SNe Ia and yields all-sky maps of the best-fit cosmological parameters with arbitrary angular resolution. To show its potential, we apply our method to the recent Union2.1 compilation, building maps with three different angular resolutions. We use a Monte Carlo method to estimate the statistical significance with which we could reject the null hypothesis that the magnitude-redshift relation is isotropic based on the properties of the observed most discrepant directions. We find that, based on pure signal-to-noise arguments, the null hypothesis cannot be rejected at any meaningful confidence level. However, if we also consider that the strongest deviations in the Union2.1 sample closely align with the dipole temperature anisotropy of the cosmic microwave background, we find that the null hypothesis should be rejected at the $95-99$ per cent confidence level, slightly depending on the angular resolution of the study. If this result is not due to a statistical fluke, it might either indicate that the SN data have not been cleaned from all possible systematics or even point towards new physics. We finally discuss future perspectives in the field for achieving larger and more uniform data sets that will vastly improve the quality of the results and optimally exploit our method.

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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