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Consistency testing for invariance of the speed of light at different redshifts: the newest results from strong lensing and Type Ia supernovae observations
Monthly Notices of the Royal Astronomical Society ( IF 4.8 ) Pub Date : 2021-07-02 , DOI: 10.1093/mnras/stab1868
Tonghua Liu 1, 2 , Shuo Cao 2, 3 , Marek Biesiada 2, 4 , Yuting Liu 2 , Yujie Lian 2 , Yilong Zhang 2
Affiliation  

The invariance of the speed of light in the distant Universe has profound significance for fundamental physics. In this paper, we propose a new model-independent method to test the invariance of the speed of light c at different redshifts by combining the strong gravitational lensing (SGL) systems and the observations of Type Ia supernovae (SNe Ia). All the quantities used to test the deviation of c come from the direct observations, and the absolute magnitudes of SNe Ia need not to be calibrated. Our results show that the speed of light in the distant Universe is no obvious deviation from the constant value c0 within the uncertainty based on current observations. Moreover, we conclude that the currently compiled SGL and SNe Ia Pantheon samples may achieve much higher precision Δc/c ∼ 10−2 for the deviation of c than all previously considered approaches. The forthcoming data from the Legacy Survey of Space and Time and Wide-Field InfraRed Space Telescope will achieve more stringent testing for deviation of the SOL (at the level of Δc/c ∼ 10−3) by using our model-independent method. Finally, we discuss the potential ways in which our technique might be improved, focusing on the treatment of possible sources of systematic uncertainties.

中文翻译:

不同红移光速不变性的一致性测试:强透镜和 Ia 型超新星观测的最新结果

遥远宇宙中光速的不变性对基础物理学具有深远的意义。在本文中,我们提出了一种新的模型无关方法,通过结合强引力透镜(SGL)系统和 Ia 型超新星(SNe Ia)的观测,来测试不同红移下光速 c 的不变性。所有用于检验 c 偏差的量均来自直接观测,超新星 Ia 的绝对星等无需校准。我们的结果表明,在当前观测的不确定性范围内,遥远宇宙中的光速与常数 c0 没有明显偏差。而且,我们得出的结论是,当前编译的 SGL 和 SNe Ia Pantheon 样本对于 c 的偏差可以实现比所有先前考虑​​的方法更高的精度 Δc/c ∼ 10-2。通过使用我们的模型无关方法,来自时空遗产调查和广域红外太空望远镜的即将到来的数据将实现更严格的 SOL 偏差测试(在 Δc/c ∼ 10-3 水平)。最后,我们讨论了改进我们的技术的潜在方法,重点是处理系统不确定性的可能来源。
更新日期:2021-07-02
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