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