  In this paper, we show how to adapt and deploy anomaly detection algorithms
based on deep autoencoders, for the unsupervised detection of new physics
signatures in the extremely challenging environment of a real-time event
selection system at the Large Hadron Collider (LHC). We demonstrate that new
physics signatures can be enhanced by three orders of magnitude, while staying
within the strict latency and resource constraints of a typical LHC event
filtering system. This would allow for collecting datasets potentially enriched
with high-purity contributions from new physics processes. Through per-layer,
highly parallel implementations of network layers, support for
autoencoder-specific losses on FPGAs and latent space based inference, we
demonstrate that anomaly detection can be performed in as little as $80\,$ns
using less than 3% of the logic resources in the Xilinx Virtex VU9P FPGA.
Opening the way to real-life applications of this idea during the next
data-taking campaign of the LHC.

Instrumentation and Detectors;High Energy Physics - Experiment