Description
- Abstract:
- As data volumes in high-energy physics continue to increase, data quality monitoring becomes increasingly important, while traditional monitoring procedures are no longer sufficient on their own. This thesis studies anomaly detection for the CMS pixel detector using machine-learning methods based on lumisection-level Data Quality Monitoring outputs. Two approaches are investigated: a variational autoencoder (VAE)-based method and a Contrastive Language-Image Pre-training (CLIP)-based method. The VAE-based method converts reconstruction loss into anomaly decisions and identifies more anomalous samples while maintaining good interpretability, although its generalization remains limited. The CLIP-based method is first tested in a few-shot setting, where its performance is weak. Its performance improves after anomaly injection is used to enlarge the training set, but it still remains below that of the VAE-based method.
- Notes:
- Thesis (Sc. M.)--Brown University, 2026
Citation
Gu, Tianshu,
"Anomaly Detection for the CMS Pixel Detector Using Machine Learning"
(2026).
Physics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:74j3netn/
