Identification of tau leptons using Deep Learning techniques at CMS

The reconstruction and identification of tau leptons decaying into hadrons are crucial for analyses with tau leptons in the final state. To discriminate hadronic tau decays from the three main backgrounds (quark or gluon induced jets, electrons, and muons), with a low rate of misidentification and with high efficiency on the signal at the same time, the information of multiple CMS sub-detectors is combined. The application of deep machine learning techniques allows to exploit the available information in a very efficient way. The introduction of a new multi-class DNN-based discriminator at CMS provides a considerable improvement of the tau identification performance with respect to the previously used BDT and cut-based discriminators.