We present a customized neural network architecture for both, slim and fat jet tagging. It is based on the idea to keep the concept of physics objects, like particle flow particles, as a core element of the network architecture. The deep learning algorithm works for most of the common jet classes, i.e. b, c, usd and gluon jets for slim jets and W, Z, H, QCD and top classes for fat jets. The developed architecture promising gains in performance as shown in simulation of the CMS collaboration. Currently the tagger is under test in real data in the CMS experiment.
|Intended contribution length||20 minutes|