  We show how event topology classification based on deep learning could be
used to improve the purity of data samples selected in real time at at the
Large Hadron Collider. We consider different data representations, on which
different kinds of multi-class classifiers are trained. Both raw data and
high-level features are utilized. In the considered examples, a filter based on
the classifier's score can be trained to retain ~99% of the interesting events
and reduce the false-positive rate by as much as one order of magnitude for
certain background processes. By operating such a filter as part of the online
event selection infrastructure of the LHC experiments, one could benefit from a
more flexible and inclusive selection strategy while reducing the amount of
downstream resources wasted in processing false positives. The saved resources
could be translated into a reduction of the detector operation cost or into an
effective increase of storage and processing capabilities, which could be
reinvested to extend the physics reach of the LHC experiments.
