Identification and online monitoring of experimental measurement states via Cuscore statistic

Abstract

We present a statistical method for detecting and analysing state changes in experimental measurements using the Cuscore statistic and its special case, the Centred Cuscore statistic. These statistics are designed to identify deviations in detector responses using sequential hypothesis testing relative to a defined reference state. Applications to charge-changing reaction experiments at the FRagment Separator facility at the GSI Helmholtz Centre for Heavy Ion Research, Germany, and the Second Radioactive Ion Beam Line in Lanzhou at the Institute of Modern Physics, China, demonstrate the ability of these tools to quantify state changes, identify the change point, and classify data segments based on measured states. For long-term online monitoring, we use the exponentially weighted moving average to continuously update computations, enabling the detection of successive changes. This method supports both real-time and post-experiment diagnostics and provides a robust approach for enhancing data integrity and experimental control in nuclear physics and related fields.

References in corpus (4)

• Machine Learning in Nuclear Physics
• A new approach for deducing rms proton radii from charge-changing reactions of neutron-rich nuclei and the reaction-target dependence
• Charge-changing cross section measurements of 300 MeV/nucleon ${}^{28}$Si on carbon and data analysis
• AI-Assisted Transport of Radioactive Ion Beams