Automated Data QA/QC Monitoring of HEP Experiments

High-energy physics (HEP) experiments and other data-intensive industries generate massive amounts of data, presenting significant challenges in ensuring data quality and operational efficiency. Existing QA/QC methods are labor-intensive, prone to human error, and unable to scale effectively with the data demands of modern research and industrial environments. To address this, Novateur proposes the development of an advanced anomaly detection and QA/QC framework, designed to automate and optimize data quality assurance for diverse applications. The system integrates cutting-edge machine learning algorithms, scalable architectures, and real-time monitoring capabilities to deliver reliable and actionable insights. The proposed system leverages innovative unsupervised anomaly detection algorithms integrated with a user-friendly graphical user interface (GUI) to deliver a robust solution for QA/QC. The system enables real-time monitoring of terabyte- to petabyte-scale datasets. Its architecture is designed for both cloud and on-premise deployment, ensuring adaptability to diverse data environments and operational requirements. Additionally, the system offers customizable settings, allowing users to configure workflows, thresholds, and alert mechanisms tailored to their specific use cases. By automating QA/QC processes, the framework enhances data reliability, reduces operational risks, and minimizes resource costs, offering significant operational insights. The primary objective of Phase II is to advance the framework from proof of concept to a fully validated and deployable solution. This will be achieved over a two-year period divided into four key milestones. First, Data Collection and Validation will involve comprehensive data acquisition and preprocessing from high-energy physics (HEP) experiments such as DUNE, NEXT, and SuperCDMS. This stage ensures robust training and testing datasets for the anomaly detection models. Next, during Algorithm Development and Testing, the detection algorithms will be refined and validated using real-world and simulated data, with performance benchmarks established against user-defined standards. Following this, GUI Development will focus on creating an intuitive and functional graphical user interface that enables seamless user interaction, configuration, and monitoring. Finally, System Deployment and Integration will involve integrating the QA/QC framework into customer workflows, conducting live deployments on user hardware at collaborating institutions, and validating system performance in operational environments. By the end of Phase II, the project will deliver a fully operational QA/QC framework validated in live HEP experimental environments. The system will demonstrate scalability and adaptability for cross-industry applications, transitioning into a commercial-ready product with a tiered subscription model to accommodate organizations of varying sizes and data needs. The successful deployment of this technology will significantly enhance data quality assurance across multiple DOE subprograms. It will drive improved operational efficiency, reduced costs, and greater reliability in research and industrial processes. Beyond its transformative impact on HEP experiments, the framework's scalability and its unsupervised approach to anomaly detection positions it to address pressing QA/QC challenges in healthcare, IoT, and finance.