Deep Representation Learning for Trigger Monitoring


We propose a novel neural network architecture called Hierarchical Latent Autoencoder to exploit the underlying hierarchical nature of the CMS Trigger System for data quality monitoring. Given the hierarchical cascaded design of the CMS Trigger System, the central idea is to learn the probability distribution of the Level 1 Triggers, modelled as the hidden archetypes, from the observable High Level Triggers. During evaluation, the learned parameters of the latent distribution can be used to generate a reconstruction probability score. We propose to use this probability metric for anomaly detection since a bounded number from zero to one has better interpretability in quantifying the severity of a fault. We selected a particular Level 1 Trigger and its corresponding High Level Triggers for our experiments. The results demonstrate that our architecture does reduce the reconstruction error on the test set from $9.35 \times 10^{-6}$ when using a vanilla Variational Autoencoder to $4.52 \times 10^{-6}$ when using our Hierarchical Latent Autoencoder. Hence, we successfully show that our custom designed architecture improves the reconstruction capability of variational autoencoders by utilizing the already existing hierarchical nature of the CMS Trigger System.

CERN Openlab Technical Report