When thousands of devices power the world’s largest experiments, even a single failure can ripple across the system. At CERN, the smooth operation of accelerators and experiments relies on the control infrastructure, which comprises thousands of technical devices working in unison. These include programmable logic controllers (PLCs), which run industrial control logic, power supplies that feed magnets and detectors, and front-end modules that manage communication between equipment and higher-level systems.

Imagine a factory or research laboratory filled with machines, each equipped with its own sensors and controllers. Traditionally, data from these devices is sent to central servers for processing. While effective, this approach often introduces delays and network bottlenecks. Industrial Edge Computing addresses the problem by bringing computation closer to where data is generated. Instead of relying on distant servers, applications run directly on edge devices installed near the equipment, allowing for real-time decision-making. These industrial PCs can host applications that collect diagnostics, perform analytics, or even take direct control of equipment. For example, a ventilation system can adjust airflow more efficiently by running advanced control algorithms locally on an edge device using nearby sensor readings.

Large technical systems such as cooling plants, ventilation systems, and safety installations must operate continuously around the clock. These systems rely on programmable logic controllers (PLCs), specialized industrial computers built to execute precise instructions reliably for years without interruption. Designed with safety and stability in mind, PLCs form the backbone of industrial automation. They manage essential functions, such as circulating cooling water for accelerators, adjusting ventilation fans to maintain safe conditions in underground tunnels, and activating interlocks to shut down equipment immediately if unsafe conditions are detected.

At CERN’s CMS experiment, data quality has traditionally been certified by human operators reviewing run after run. However, with the increasing volume of data, this manual process is becoming a bottleneck, paving the way for machine learning to take a more prominent role.

To ensure datasets are reliable for physics analyses, the CMS collaboration uses Data Quality Monitoring (DQM) software. This software analyzes raw detector output and generates concise summaries, known as monitor elements. These include histograms of sensor signals, basic statistics about detector performance, and plots that highlight unusual or unexpected behavior.

Every second, the CMS tracker detector records millions of particle trajectories. But without careful quality checks, this flood of data risks being unusable for physics discoveries. To guard against this, CMS relies on a dedicated Data Quality Monitoring (DQM) system. The tracker, functioning like a giant digital camera, captures the paths of charged particles produced during collisions. Because it generates such an enormous volume of data, ensuring quality is essential for reliable physics analyses.