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Background treatment is crucial to extract physics from precision experiments. In this paper, we introduce a novel method to assign each event a signal probability. This could then be used to weight the events contribution to the likelihood during fitting. To illustrate the effect of this method, we test it with MC samples. The consistence between the constructed background and the background from MC truth shows that the background subtraction method with probabilistic event weights is feasible in partial wave analysis at BES III.
A common situation in experimental physics is to have a signal which can not be separated from a non-interfering background through the use of any cut. In this paper, we describe a procedure for determining, on an event-by-event basis, a quality fact
We discuss a new method to extract neutrino signals in low energy experiments. In this scheme the symmetric nature of most backgrounds allows for direct cancellation from data. The application of this technique to the Palo Verde reactor neutrino osci
textsc{Jewel} is a fully dynamical event generator for jet evolution in a dense QCD medium, which has been validated for multiple jet and jet-like observables. Jet constituents (partons) undergo collisions with thermal partons from the medium, leadin
In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network paramete
One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to express