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In this work a self-organizing map (SOM) is applied for electron/jet identification in the highlevel trigger of ATLAS detector, using calorimeter data mapped over Regions of Interest (RoI). For feature extraction the SOM is unsupervised trained and the resulting map is slightly modified through Learning Vector Quantization (LVQ) algorithm for optimal signal identification. In practice, calorimeter designs exhibit nonlinearities so a nonlinear feature extraction technique, like SOM, is attractive. The hypothesis testing is performed by a neural classifier implemented through a Multi-layer Perceptron (MLP) architecture. Using the proposed technique for an electron/jet discrimination in the second level trigger of ATLAS, 98% of electrons are correctly identified for a misclassification of jets below 3%, which outperforms the baseline algorithm presently used in ATLAS.

Self-organized mapping of calorimetry information for high efficient online electron/jet identification in ATLAS