Vertex identification optimization in the Higgs to gamma gamma decay channel

A study of vertex identification efficiency in the Higgs to gamma gamma channel has been performed using boosted decision tree multivariate classification. The analysis tests the performance of a photon time of flight discriminant as an additional variable in classification. All training is done on Monte Carlo events with 14 TeV collisions, 50 pile up events, and a Higgs mass of 125 GeV, from both gluon-gluon fusion and vector boson fusion production. The algorithm is designed for a time resolution of 0.01 nanoseconds, requiring the addition of a high precision timing layer for implementation. Preliminary efficiency increases in individualized detector regions motivates further study of this algorithm for use in future analyses.