Speaker
Description
In the track reconstruction in the CMS software, particle tracks are determined using a Combinatorial Track Finder algorithm. In order to optimize the speed and accuracy of the algorithm, the tracks are reconstructed using an iterative process: Easiest tracks are searched first, then hits, associated to good found tracks, are excluded from consideration in the following iterations (masking) before continuing with the next iteration. At the end of each iteration, a track selection is performed to classify different tracks depending on their quality. Currently we use classifiers (one for each iteration) based on a shallow Boosted-Decision-Tree whose inputs variables are track features, such as the goodness-of-fit and number of hits. To enhance the performance of this classification, we have developed a novel classifier based on a deep neural network trained using the TensorFlow framework. This new technique not only performs better, it also has the advantage to use a single classifier for all iterations: this simplifies the task of retraining the classifier and to maintain its high performance in the changing conditions of the detector. In this talk we will present the characteristic and performance of the new deep neural network classifier. We will also discuss the impact on both training and inference of changing some of the properties of the network such as topology, score function and input parameters.
