?
Unsupervised Learning for Calorimeter Response Correction: A WGAN-Based Method
The long-term stability of calorimeters is crucial in high-energy physics experiments, where precise energy measurements are essential for accurate particle reconstruction. This study introduces a Wasserstein GAN (WGAN)-based machine learning approach for calibrating calorimeter responses affected by aging and other systematic shifts. Our methodology is applied to realistic, high-granularity calorimeter data that more accurately mimic physical detector conditions. The dataset reflects energy deposition across all calorimeter cells, following an exponential energy spectrum and eliminating artificial peaks in the distribution. By leveraging Wasserstein distance minimization, our model estimates aging coefficients of cells, realigning degraded detector responses with their undamaged counterparts. The results highlight the potential of a data-driven approach for calorimeter calibration, demonstrating correcting energy measurement discrepancies with a reduced number of required events, making it a valuable tool for future detector calibration strategies.