Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

TL;DR

This paper explores transfer learning for anomaly detection in high-dimensional spatio-temporal data from CERN's Hadron Calorimeter, demonstrating improved accuracy and efficiency with limited training data using a hybrid neural network architecture.

Contribution

It introduces a novel transfer learning approach for complex spatio-temporal anomaly detection in high-dimensional sensor data, with insights into model initialization and training configurations.

Findings

Transfer learning improves model accuracy with limited data.

Hybrid neural networks effectively detect anomalies in high-dimensional sensor data.

Transferability of models varies across different sections of the detector.

Abstract

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL's potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects. Code: https://github.com/muleina/CMS\_HCAL\_ML\_OnlineDQM .