  Supervised artificial neural networks with the rapidity-mass matrix (RMM)
inputs were studied using several Monte Carlo event samples for various pp
collision processes. The study shows the usability of this approach for general
event classification problems. The proposed standardization of the ANN feature
space can simplify searches for signatures of new physics at the LHC when using
machine learning techniques. In particular, we illustrate how to improve
signal-over-background ratios in searches for new physics, how to filter out
Standard Model events for model-agnostic searches, and how to separate gluon
and quark jets for Standard Model measurements.
