  We present a two-stage neural network architecture that enables a fully
autonomous and comprehensive characterization of collision events by
exclusively exploiting the four momenta of final-state particles. We refer to
the first stage of the architecture as Lorentz Boost Network (LBN). The LBN
allows the creation of particle combinations representing rest frames. The LBN
also enables the formation of further composite particles, which are then
transformed into said rest frames by Lorentz transformation. The properties of
the composite, transformed particles are compiled in the form of characteristic
variables that serve as input for a subsequent network. This second network has
to be configured for a specific analysis task such as the separation of signal
and background events. Using the example of the classification of ttH and ttbb
events, we compare the separation power of the LBN approach with that of
domain-unspecific deep neural networks (DNN). We observe leading performance
with the LBN, even though we provide the DNNs with extensive additional input
information beyond the particle four momenta. Furthermore, we demonstrate that
the LBN forms physically meaningful particle combinations and autonomously
generates suitable characteristic variables.
