Nguyen, Thong QWeitekamp, DanielAnderson, DustinCastello, RobertoCerri, OlmoPierini, MaurizioSpiropulu, MariaVlimant, Jean-Roch2018-07-032018-07-032018-07-032019-08-3110.1007/s41781-019-0028-1https://infoscience.epfl.ch/handle/20.500.14299/1470581807.00083We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time 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 more than one order of magnitude. 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 translate 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.text::journal::journal article::research article