Data-Mining-Based Detector Calibrations for High-Energy Physics

Abstract

The generation of Dead Hot Maps (DHMs) is an important part of PHENIX data analysis, as it helps reduce detector-related biases. During this step, detector units with abnormal behavior are identified, such as dead, hot, or extremely hot channels. Removing these channels from the analysis prevents detector effects from changing the measured collision distributions. This helps ensure that the final data sample represents the underlying collision physics more reliably. In this work, the standard DHM construction procedure is compared with anomaly detection methods that are commonly used in data analysis and data mining. These methods can offer simpler and faster alternatives to the conventional approach, with lower computational demand and less need for manual work. Five techniques were tested: Z-score analysis, Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Kernel Density Estimation (KDE). The main goal was to examine which method gives a detector mask that is closest to the established statistical reference and is also physically reasonable. The results suggest that these anomaly detection approaches may be useful for detector monitoring and calibration tasks. Some of the tested methods are easy to apply, computationally efficient, and do not require special hardware. Because of this, they could be included in future automated detector quality control workflows. Their simple implementation also makes them suitable for use in different software environments. In this way, these methods may support faster and more consistent identification of anomalous detector towers.