Summary of Data Quality Monitoring Through Transfer Learning on Anomaly Detection For the Hadron Calorimeters, by Mulugeta Weldezgina Asres et al.
Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters
by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, Pavel Parygin, David Yu, Jay Dittmann, CMS-HCAL Collaboration
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study explores the potential of transfer learning (TL) for anomaly detection (AD) applications in spatio-temporal (ST) data, specifically in the context of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The research leverages TL mechanisms to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. By transferring ST AD models trained on data collected from one part of a calorimeter to another, the study demonstrates that TL effectively enhances model learning accuracy on a target subdetector. The experiment results show that TL achieves promising data reconstruction and AD performance while substantially reducing the trainable parameters of the AD models. Additionally, the study highlights the improvement in robustness against anomaly contamination in the training data sets of the semi-supervised AD models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to use old machine learning models for new tasks. It takes data from sensors that measure things like energy and position in a big experiment at CERN. The goal is to find unusual patterns in this data, which can help scientists detect problems or predict when something might go wrong. The researchers tried using pre-trained models (like a kind of “template” model) for different types of data analysis tasks. They found that these templates were very good at helping the new models learn faster and more accurately. This could make it easier to analyze big datasets without needing as much training data. |
Keywords
» Artificial intelligence » Anomaly detection » Machine learning » Semi supervised » Transfer learning
