Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider

To study the physics of fundamental particles and their interactions, the Large Hadron Collider was constructed at CERN, where protons collide to create new particles measured by detectors. Collisions occur at a frequency of 40 MHz, and with an event size of roughly 1 MB it is impossible to read out and store the generated amount of data from the detector and therefore a multi-tiered, real-time filtering system is required. In this paper, we show how to adapt and deploy deep-learning-based autoencoders for the unsupervised detection of new physics signatures in the challenging environment of a real-time event selection system at the Large Hadron Collider. The first-stage filter, implemented on custom electronics, decides within a few microseconds whether an event should be kept or discarded. At this stage, the rate is reduced from 40 MHz to about 100 kHz. We demonstrate the deployment of an unsupervised selection algorithm on this custom electronics, running in as little as 80 ns and enhancing the signal-over-background ratio by three orders of magnitude. This work enables the practical deployment of these networks during the next data-taking campaign of the Large Hadron Collider. The Large Hadron Collider produces 40 million collision events per second, most of which need to be discarded by a real-time filtering system. Unsupervised deep learning algorithms are developed and deployed on custom electronics to search for rare events indicating new physics, rather than for specific events led by theory.