Bayesian inference for near-field interferometric tests of collapse models

TL;DR

This paper uses Bayesian inference to analyze matterwave interferometry experiments with large masses, aiming to test and constrain collapse models like CSL, and provides guidelines for optimizing experimental parameters.

Contribution

It introduces a Bayesian framework to evaluate decoherence effects and optimize experimental design for near-field interferometry testing collapse models.

Findings

Feasibility of reaching masses around 10^9 u in experiments.

Quantitative bounds on collapse model parameters like CSL.

Guidelines for experimental optimization and measurement requirements.

Abstract

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 $\sim10^9\,\text{u}$ 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.