  The main b-physics trigger algorithm used by the LHCb experiment is the
so-called topological trigger. The topological trigger selects vertices which
are a) detached from the primary proton-proton collision and b) compatible with
coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which
utilized a custom boosted decision tree algorithm, selected a nearly 100% pure
sample of b-hadrons with a typical efficiency of 60-70%; its output was used in
about 60% of LHCb papers. This talk presents studies carried out to optimize
the topological trigger for LHC Run 2. In particular, we have carried out a
detailed comparison of various machine learning classifier algorithms, e.g.,
AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is
designed to select all "interesting" decays of b-hadrons, but cannot be trained
on every such decay. Studies have therefore been performed to determine how to
optimize the performance of the classification algorithm on decays not used in
the training. Methods studied include cascading, ensembling and blending
techniques. Furthermore, novel boosting techniques have been implemented that
will help reduce systematic uncertainties in Run 2 measurements. We demonstrate
that the reoptimized topological trigger is expected to significantly improve
on the Run 1 performance for a wide range of b-hadron decays.
