Data blinding for the nEDM experiment at PSI
N. J. Ayres, G. Ban, G. Bison, K. Bodek, V. Bondar, E. Chanel, P.-J., Chiu, C. Crawford, M. Daum, S. Emmenegger, L. Ferraris-Bouchez, P. Flaux, P.G, Harris, Z. Gruji\'c, N. Hild, J. Hommet, B. Lauss, T. Lefort, Y. Lemiere, M., Kasprzak, Y. Kermaidic, K. Kirch, S. Komposch

TL;DR
This paper introduces a novel data blinding algorithm for neutron electric dipole moment experiments, enabling unbiased analysis by subtly modifying data without revealing the blinding process, thus enhancing the credibility of results.
Contribution
The paper presents a flexible, automated data blinding method tailored for complex experiments like nEDM, allowing multiple independent analysis stages without bias.
Findings
Successfully applied to 2015/2016 nEDM data
Allows multiple independent analysis teams
Enables re-blinding without revealing the secret
Abstract
Psychological bias towards, or away from, a prior measurement or a theory prediction is an intrinsic threat to any data analysis. While various methods can be used to avoid the bias, e.g. actively not looking at the result, only data blinding is a traceable and thus trustworthy method to circumvent the bias and to convince a public audience that there is not even an accidental psychological bias. Data blinding is nowadays a standard practice in particle physics, but it is particularly difficult for experiments searching for the neutron electric dipole moment, as several cross measurements, in particular of the magnetic field, create a self-consistent network into which it is hard to inject a fake signal. We present an algorithm that modifies the data without influencing the experiment. Results of an automated analysis of the data are used to change the recorded spin state of a few…
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