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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 neutrons of each measurement cycle. The flexible algorithm is applied twice to the data, to provide different data to various analysis teams. This gives us the option to sequentially apply various blinding offsets for separate analysis steps with independent teams. The subtle modification of the data allows us to modify the algorithm and to produce a re-blinded data set without revealing the blinding secret. The method was designed for the 2015/2016 measurement campaign of the nEDM experiment at the Paul Scherrer Institute. However, it can be re-used with minor modification for the follow-up experiment n2EDM, and may be suitable for comparable efforts.
We report on the strategy used to optimize the sensitivity of our search for a neutron electric dipole moment at the Paul Scherrer Institute. Measurements were made upon ultracold neutrons stored within a single chamber at the heart of our apparatus.
The GERDA and Majorana experiments will search for neutrinoless double-beta decay of germanium-76 using isotopically enriched high-purity germanium detectors. Although the experiments differ in conceptual design, they share many aspects in common, an
A selection of unfolding methods commonly used in High Energy Physics is compared. The methods discussed here are: bin-by-bin correction factors, matrix inversion, template fit, Tikhonov regularisation and two examples of iterative methods. Two proce
The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics ana
The Collaborative Analysis Versioning Environment System (CAVES) project concentrates on the interactions between users performing data and/or computing intensive analyses on large data sets, as encountered in many contemporary scientific disciplines