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

arXiv: https://arxiv.org/abs/2408.16612 · HTML: https://mlsanity.com/paper/2408.16612/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [L2 Regularization versus Batch and Weight Normalization](https://mlsanity.com/paper/1706.05350/) (2017)
- [Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/1905.13628/) (2019)
- [A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis](https://mlsanity.com/paper/2209.04635/) (2022)
- [Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider](https://mlsanity.com/paper/1808.00911/) (2018)
- [Classification of chaotic time series with deep learning](https://mlsanity.com/paper/1908.06848/) (2019)
- [Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks](https://mlsanity.com/paper/1807.01705/) (2018)
- [Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter](https://mlsanity.com/paper/2311.04190/) (2023)
- [Anomaly Detection on Graph Time Series](https://mlsanity.com/paper/1708.02975/) (2017)
- [Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments](https://mlsanity.com/paper/2405.15508/) (2024)

## Built on by (most cited first, 2 of 2)

- [Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data](https://mlsanity.com/paper/2412.11800/) (2024)
- [MEDIC: a network for monitoring data quality in collider experiments](https://mlsanity.com/paper/2511.18172/) (2025)

## Abstract (arXiv)

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/CMSHCALMLOnlineDQM .