Classification of the impact parameter in nucleus–nucleus collisions by a support vector machine method

One of the problems in the analysis of nucleus–nucleus collisions is to get information on the value of the impact parameter b. This work consists in the application of pattern recognition techniques aimed at associating values of b to groups of events. To this end, a support vector machine (SVM) classifier is adopted to analyse multifragmentation reactions. This method allows us to acktrace the values of b through a particular multidimensional analysis. The SVM classification consists of two main phases. In the first one, known as the training phase, the classifier learns to discriminate events that are generated by a model. In this case we used a classical molecular dynamics (CMD) model for the reaction: 58Ni + 48Ca at 25 A MeV. To check the classification of events in the second one, known as the test phase, what has been learned is tested on newevents generated by the same model. These new results have been compared to those obtained through others techniques of backtracing the impact parameter (estimate function of b and PCA analysis). This approach better classifies central and peripheral collisions with respect to other techniques. We have finally performed the SVM classification on the experimental data measuredby the NUCL-EX Collaboration with CHIMERA apparatus for the previous reaction and we show some results of the method.

One of the problems in the analysis of nucleus-nucleus collisions is to get information on the value of the impact parameter b. This work consists in the application of pattern recognition techniques aimed at associating values of b to groups of events. To this end, a support vector machine (SVM) classifier is adopted to analyse multifragmentation reactions. This method allows us to backtrace the values of b through a particular multidimensional analysis. The SVM classification consists of two main phases. In the first one, known as the training phase, the classifier learns to discriminate events that are generated by a model. In this case we used a classical molecular dynamics (CMD) model for the reaction: (58)Ni + (48)Ca at 25 A MeV. To check the classification of events in the second one, known as the test phase, what has been learned is tested on new events generated by the same model. These new results have been compared to those obtained through others techniques of backtracing the impact parameter ( estimate function of b and PCA analysis). This approach better classifies central and peripheral collisions with respect to other techniques. We have finally performed the SVM classification on the experimental data measured by the NUCL-EX Collaboration with CHIMERA apparatus for the previous reaction and we show some results of the method.