Bayesian inference for near-field interferometric tests of collapse models
Bayesian inference for near-field interferometric tests of collapse models
复制标题
塌陷模型近场干涉测试的贝叶斯推理
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
James Bateman
中科院分区：
文献类型：
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作者：
Shaun Laing;James Bateman
我们探讨了提出的大测试质量物质波干涉实验所提供的关于量子力学的可参数扩展的信息，例如已经提出的解释表观量子到经典跃迁的信息。具体地说，我们考虑了物质波近场Talbot干涉仪和连续自发定域(CSL)。利用贝叶斯推理，我们计算了包括压力和黑体辐射在内的退相干机制的影响，找到了所需测量次数的估计，并提供了一种优化选择实验控制变量的方法。我们证明了在一个类似MAQRO的实验中，有可能达到$sim109^9，Ext{u}的质量，并且我们量化了可以对CSL施加的界限。这些具体的结果可以用来指导实验设计，一般方法也可以应用于其他可参数化模型。
We explore the information which proposed matterwave interferometry experiments with large test masses can provide about parameterizable extensions to quantum mechanics, such as have been proposed to explain the apparent quantum to classical transition. Specifically, we consider a matterwave near-field Talbot interferometer and Continuous Spontaneous Localisation (CSL). Using Bayesian inference we compute the effect of decoherence mechanisms including pressure and blackbody radiation, find estimates for the number of measurements required, and provide a procedure for optimal choice of experimental control variables. We show that in a MAQRO like experiment it is possible to reach masses of $sim10^9, ext{u}$ and we quantify the bounds which can be placed on CSL. These specific results can be used to inform experimental design and the general approach can be applied to other parameterizable models.