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Application of Gene Expression Programming to Event Selection in High Energy Physics

Application of Gene Expression Programming to Event Selection in High Energy Physics,Liliana Teodorescu,Ivan D. Reid

Application of Gene Expression Programming to Event Selection in High Energy Physics   (Citations: 2)
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Gene Expression Programming is a new evolutionary algorithm found to be very efcient for solving benchmark problems from computer science. The algorithm was successfully tested for event selection in high energy physics for the KS π π decay process. This paper presents an extended version of this analysis, as well as a comparison of its results with those obtained with an Articial Neural Network and a Boosted Decision Trees method. All three methods pro- duced selection functions which allowed good signal and background separation for the problem investigated, with the classication accuracies higher than 95%.
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    • ...Comparative studies of event selection with GEP and with commonly used methods in high energy physics were presented in [15]...
    • ...The selection of KS particle produced in e + e interaction at 10GeV and reconstructed in the decay mode KS ! � + � was used as an example application [14], [15]...

    Liliana Teodorescu. Evolutionary Computation in High Energy Physics

    • ...The applicability of GEP to particle physics data analysis was investigated in our previous work [4] [5] [6] for an event selection (signal-background discrimination) problem...
    • ...In order to be able to rely on the conclusions of the previous studies ([4] [5] [6]) and to perform meaningful comparisons, the same datasets as in these previous studies were used...
    • ...It was previously demonstrated [6] that, for this problem, the increase of the number of events or of the number of event variables in the dataset do not improve the quality of the solution...

    Liliana Teodorescuet al. Enhanced Gene Expression Programming for signal-background discriminat...

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