Explaining Counts from EPRB Experiments: Are They Consistent with Quantum Theory?
arXiv:1112.3399
Abstract
I have fit data from EPRB experiments of Weihs et al to a model that consists of an EPRB thought experiment whose output is filtered. The filter implements the assumptions made by most investigators: that EPRB experiments satisfy a fair sampling assumption; and that detections and coincidences occur randomly with Poisson distributions (this provides a basis for calculating standard errors of coincidence counts, correlations, and so forth). The model does not fit the data-predicted and observed counts of detections and coincidences differ far too much, by a chi-square criterion. Logically, one must give up fair sampling and/or Poisson errors and/or the assumption that the data derive ultimately from an EPRB thought experiment. In the literature, giving up fair sampling seems to be coupled to giving up EPRB, but to me it seems just as sensible to keep EPRB and to give up fair sampling and Poisson errors. Some rather ordinary mechanisms can violate fair sampling, and there is the possibility in any experiment for uncontrolled and unmonitored factors to contribute to unwanted variation. By sufficiently relaxing the fair sampling and Poisson assumptions-it doesn't take much-my model can be made to fit the data.
25 pages, including 8 figures
References in corpus (10)
- Is the Fair Sampling Assumption supported by EPR Experiments?
- Detection loophole in asymmetric Bell experiments
- Violation of local realism vs detection efficiency
- Einstein-Podolsky-Rosen-Bohm laboratory experiments: Data analysis and simulation
- Anomalies in experimental data for the EPR-Bohm experiment: Are both classical and quantum mechanics wrong?
- The "Chaotic Ball" model,local realism and the Bell test loopholes
- Simulation of Einstein-Podolsky-Rosen experiments in a local hidden variables model with limited efficiency and coherence
- Bell correlations and equal time measurements
- Numerical simulation of Einstein-Podolsky-Rosen experiments in a local hidden variables model
- Using Linear Programming to Construct Better Criteria for Closing the Detection Loophole in Epr Experiments