Cosmic Bell Test: Measurement Settings from Milky Way Stars.

Bell's theorem states that some predictions of quantum mechanics cannot be reproduced by a local-realist theory. That conflict is expressed by Bell's inequality, which is usually derived under the assumption that there are no statistical correlations between the choices of measurement settings and anything else that can causally affect the measurement outcomes. In previous experiments, this "freedom of choice" was addressed by ensuring that selection of measurement settings via conventional "quantum random number generators" was spacelike separated from the entangled particle creation. This, however, left open the possibility that an unknown cause affected both the setting choices and measurement outcomes as recently as mere microseconds before each experimental trial. Here we report on a new experimental test of Bell's inequality that, for the first time, uses distant astronomical sources as "cosmic setting generators." In our tests with polarization-entangled photons, measurement settings were chosen using real-time observations of Milky Way stars while simultaneously ensuring locality. Assuming fair sampling for all detected photons, and that each stellar photon's color was set at emission, we observe statistically significant ≳7.31σ and ≳11.93σ violations of Bell's inequality with estimated p values of ≲1.8×10^{-13} and ≲4.0×10^{-33}, respectively, thereby pushing back by ∼600 years the most recent time by which any local-realist influences could have engineered the observed Bell violation.

Supervised Detection of Connector Lock Events with Optical Microphone Data.

In manufacturing industry, one of the main targets is to increase automation and ultimately to avoid failures under all circumstances. The plugging and locking of connectors is a class of tasks which is yet hard to be automatized with sufficiently high process stability. Due to the variation of plugging positions and external disturbances, e.g. occlusion due to cables, the quality assessment of plugging processes has emerged as a challenging task for image-based systems. For this reason, the proposed approach analyzes the inherent acoustic connector locking properties in combination with different neural network architectures in order to correctly identify connector locking signals and further to distinguish them from other machining events occurring in assembly plants. For this specific task, highly sensitive optical microphones have been applied for data acquisition. The proposed experiments are carried out under laboratory conditions as well as for the more complex situation in a real manufacturing environment. In this context, the usage of multimodal neural network architectures achieved highest levels in classification performance with accuracy levels close to 90%.