论文标题

现有测量中的α,盲和偏见变化

Varying alpha, blinding, and bias in existing measurements

论文作者

Lee, Chung-Chi, Webb, John K., Carswell, Robert F., Dzuba, Vladimir A., Flambaum, Victor V., Milaković, Dinko

论文摘要

VLT上的高分辨率光谱仪板允许以前所未有的精度测量基本常数,因此可以对某些理论预测的时空变化进行测试。在最近的一系列论文中,我们制定了最佳分析程序,既可以揭示并消除先前的类星体吸收系统测量值的主观性和偏差。在本文中,我们分析了z_ {abs} = 1.15的吸收系统的意式浓缩光谱,朝着类星体HE0515-4414。我们的目标不是在该系统中提供对精细结构常数Alpha的新的无偏测量(将单独完成)。相反,它是在Murphy(2022)的最新数据分析中仔细检查在其他几项分析中对相同数据的盲目程序的影响。为此,我们使用超级计算机Monte Carlo AI计算来生成大量独立构建的吸收络合物模型。每个模型均使用AI-VPFIT获得,并用alpha固定直到获得“最终”模型,然后将Alpha释放为一个最终优化的免费参数。结果表明,α的“测量”值系统地偏向最初固定的值,即此过程产生毫无意义的测量值。含义很简单:为了避免偏见,所有将来的测量都必须将alpha作为自由参数,从建模过程开始。

The high resolution spectrograph ESPRESSO on the VLT allows measurements of fundamental constants at unprecedented precision and hence enables tests for spacetime variations predicted by some theories. In a series of recent papers, we developed optimal analysis procedures that both exposes and eliminates the subjectivity and bias in previous quasar absorption system measurements. In this paper we analyse the ESPRESSO spectrum of the absorption system at z_{abs}=1.15 towards the quasar HE0515-4414. Our goal here is not to provide a new unbiased measurement of fine structure constant, alpha, in this system (that will be done separately). Rather, it is to carefully examine the impact of blinding procedures applied in the recent analysis of the same data by Murphy (2022) and prior to that, in several other analyses. To do this we use supercomputer Monte Carlo AI calculations to generate a large number of independently constructed models of the absorption complex. Each model is obtained using AI-VPFIT, with alpha fixed until a "final" model is obtained, at which point alpha is then released as a free parameter for one final optimisation. The results show that the "measured" value of alpha is systematically biased towards the initially-fixed value i.e. this process produces meaningless measurements. The implication is straightforward: to avoid bias, all future measurements must include alpha as a free parameter from the beginning of the modelling process.

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