  We study several simplified dark matter (DM) models and their signatures at
the LHC using neural networks. We focus on the usual monojet plus missing
transverse energy channel, but to train the algorithms we organize the data in
2D histograms instead of event-by-event arrays. This results in a large
performance boost to distinguish between standard model (SM) only and SM plus
new physics signals. We use the kinematic monojet features as input data which
allow us to describe families of models with a single data sample. We found
that the neural network performance does not depend on the simulated number of
background events if they are presented as a function of $S/\sqrt{B}$, where
$S$ and $B$ are the number of signal and background events per histogram,
respectively. This provides flexibility to the method, since testing a
particular model in that case only requires knowing the new physics monojet
cross section. Furthermore, we also discuss the network performance under
incorrect assumptions about the true DM nature. Finally, we propose multimodel
classifiers to search and identify new signals in a more general way, for the
next LHC run.
