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The upgrade of the track classification and selection step of the CMStracking to a Deep Neural Network is presented. The CMS tracking follows aniterative approach: tracks are reconstructed in multiple passes starting fromthe ones that are easiest to find and moving to the ones with more complexcharacteristics (lower transverse momentum, high displacement). The trackclassification comes into play at the end of each iteration. A classifier usinga multivariate analysis is applied after each iteration and several selectioncriteria are defined. If a track meets the high purity requirement, its hitsare removed from the hit collection, thus simplifying the later iterations, andmaking the track classification an integral part of the reconstruction process.Tracks passing loose selections are also saved for physics analysis usage. TheCMS experiment improved the track classification starting from a parametricselection used in Run 1, moving to a Boosted Decision Tree in Run 2, andfinally to a Deep Neural Network in Run 3. An overview of the Deep NeuralNetwork training and current performance is shown.

A DNN for CMS track classification and selection