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We present a first proof of concept to directly use neural network basedpattern recognition to trigger on distinct calorimeter signatures fromdisplaced particles, such as those that arise from the decays of exoticlong-lived particles. The study is performed for a high granularity forwardcalorimeter similar to the planned high granularity calorimeter for the highluminosity upgrade of the CMS detector at the CERN Large Hadron Collider.Without assuming a particular model that predicts long-lived particles, we showthat a simple convolutional neural network, that could in principle be deployedon dedicated fast hardware, can efficiently identify showers from displacedparticles down to low energies while providing a low trigger rate.

Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter