Methods for a blind analysis of isobar data collected by the STAR collaboration

In 2018,the STAR collaboration collected data from^(96)_(44)Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr at√^(S)NN=200 Ge V to search for the presence of the chiral magnetic effect in collisions of nuclei.The isobar collision species alternated frequently between 9644 Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr.In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data,STAR developed a three-step blind analysis procedure.Analysts are initially provided a"reference sample"of data,comprised of a mix of events from the two species,the order of which respects time-dependent changes in run conditions.After tuning analysis codes and performing time-dependent quality assurance on the reference sample,analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual≈30-min data-taking runs.For this sample,species-specific information is disguised,but individual output files contain data from a single isobar species.Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage.Following these modifications,the"frozen"code is passed over the fully un-blind data,completing the blind analysis.As a check of the feasibility of the blind analysis procedure,analysts completed a"mock data challenge,"analyzing data from Au+Au collisions at√^(S)NN=27 Ge V,collected in 2018.The Au+Au data were prepared in the same manner intended for the isobar blind data.The details of the blind analysis procedure and results from the mock data challenge are presented.

RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).

Measurment of gas-liquid two-phase slug flow with a Venturi meter based on blind source separation

We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source separation technique. The flow measurement model is established based on the fluctuation characteristics of differential pressure(DP) signals measured from a Venturi meter. It is demonstrated that DP signals of two-phase flow are a linear mixture of DP signals of single phase fluids. The measurement model is a combination of throttle relationship and blind source separation model. In addition, we estimate the mixture matrix using the independent component analysis(ICA) technique. The mixture matrix could be described using the variances of two DP signals acquired from two Venturi meters. The validity of the proposed model was tested in the gas–liquid twophase flow loop facility. Experimental results showed that for most slug flow the relative error is within 10%.We also find that the mixture matrix is beneficial to investigate the flow mechanism of gas–liquid two-phase flow.