  We study the prospects of characterising Dark Matter at colliders using
Machine Learning (ML) techniques. We focus on the monojet and missing
transverse energy (MET) channel and propose a set of benchmark models for the
study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a
pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light
Dark Matter impostor whose interactions are mediated by a heavy particle. All
these benchmarks are tensioned against each other, and against the main SM
background ($Z$+jets). Our analysis uses both the leading-order kinematic
features as well as the information of an additional hard jet. We explore
different representations of the data, from a simple event data sample with
values of kinematic variables fed into a Logistic Regression algorithm or a
Fully Connected Neural Network, to a transformation of the data into images
related to probability distributions, fed to Deep and Convolutional Neural
Networks. We also study the robustness of our method against including detector
effects, dropping kinematic variables, or changing the number of events per
image. In the case of signals with more combinatorial possibilities (events
with more than one hard jet), the most crucial data features are selected by
performing a Principal Component Analysis. We compare the performance of all
these methods, and find that using the 2D images of the combined information of
multiple events significantly improves the discrimination performance.
