Automated Data QA/QC Monitoring of HEP Experiments

Currently, each new experiment typically requires the development of a custom solution for QA/QC, leading to repeated work and inefficiencies. We propose an automated QA/QC system that leverages machine learning algorithms for real- time monitoring of HEP experiments. Our "Grow-When-Required" (GWR) model, integrated with Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, will continually adapt to incoming data, thus providing scalable, real-time anomaly detection. In Phase I, we will develop a proof-of-concept of the GWR+UMAP model and test its efficiency on smaller HEP datasets. We will also compare its performance against current QA/QC methods. Based on these results, we will refine our algorithms and prepare for scalability in Phase II. The automated QA/QC system is not limited to HEP experiments and has broader applications in sectors like energy, healthcare, and meteorology. Successful implementation in Phase II and III would lead to significantly reduced human errors, cost savings, and enhanced public services. The technology would enable the DOE and other agencies to make more efficient use of their resources.