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