Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks

Abstract

We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electronics effects, events are simulated in which a single charged pion is shot at a homogenous lead glass calorimeter, split into longitudinal and transverse segments of varying size. As an approximation of an optimal reconstruction, a neural network-based energy regression is trained. The architecture is based on blocks of convolutional kernels customized for shower energy regression using local energy densities; biases at the edges of the training dataset are mitigated using a histogram technique. With this approximation, we find that a longitudinal and transverse segment size less than or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is necessary to achieve an optimal energy measurement. In addition, an intrinsic energy resolution of $8\%/\sqrt{E}$ for pion showers is observed.

References in corpus (3)

• Particle-flow reconstruction and global event description with the CMS detector
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• Hadronic energy resolution of a highly granular scintillator-steel hadron calorimeter using software compensation techniques

Cited by in corpus (7)

• On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters
• A high-granularity calorimeter insert based on SiPM-on-tile technology at the future Electron-Ion Collider
• Design of a SiPM-on-Tile ZDC for the future EIC and its Performance with Graph Neural Networks
• Unifying Simulation and Inference with Normalizing Flows
• Leveraging Staggered Tessellation for Enhanced Spatial Resolution in High-Granularity Calorimeters
• Fighting Newtonian Noise with Gradient-Based Optimization at the Einstein Telescope
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations