Identification of new long-lived particles using deep neural networks

We present the development of a deep neural network for identifying generic displaced jets arising from the decays of exotic long-lived particles in data recorded by the CMS detector at the CERN LHC. Various jet features including detailed information about each clustered particle candidate as well as reconstructed secondary vertices are refined through the use of 1-dimensional convolution layers before being combined with high-level engineered features and passed through a series of fully-connected layers. The proper lifetime of the long-lived particle, cτ0, is treated as a parameter of the neural network model, which allows for hypothesis testing over several orders of magnitude ranging from cτ0 = 1 µm to 10 m. Domain adaptation by backward propagation is performed to construct domain-independent features at an intermediate layer of the network to mitiage difference between simulation and data. The training is performed by streaming ROOT trees containing O(100M) jets directly into the TensorFlow queue system, which allows for a flexible selection of input features and asynchronous preprocessing. The application of the tagger is showcased in a search for long-lived gluinos as predicted by split supersymmetric models demonstrating significant gains in sensitivity over a reference analysis.