Energy Correction in Reduced SiD Electromagnetic Calorimeter
Lucas Braun, Jason T. Barkeloo, James E. Brau, Christopher T., Potter

TL;DR
This paper presents a neural network-based method to estimate and correct for energy leakage in a reduced SiD electromagnetic calorimeter, improving measurement accuracy and resolution for electrons and photons.
Contribution
It introduces a novel neural network approach for energy correction in a smaller SiD ECal design, enabling accurate energy measurements despite fewer layers.
Findings
Significant improvement in energy measurement accuracy and resolution.
Effective correction across various electron energies and angles.
Viability of using a smaller, less expensive ECal with precise energy correction.
Abstract
SiD is a robust, silicon-based detector proposed for the International Linear Collider (ILC). SiD employs a sampling silicon-tungsten electromagnetic calorimeter (ECal) to accurately measure the energies of electrons, positrons, and photons produced in collisions, and to contribute to jet energy measurements through the particle flow technique. Due to the nature of the detector and its design constraints, a portion of the electron, positron, and photon energy exits the ECal undetected. Here, we establish a methodology for estimating the exiting energy and correctly determining the energies of electrons (and photons) in the ECal by analyzing patterns in the total energy deposition in each layer using neural networks. We studied a reduced calorimeter design with fewer layers (16 thin layers, 8 thick layers) than the proposed SiD design (20 thin layers, 10 thick layers) to evaluate if the…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Superconducting and THz Device Technology · Interconnection Networks and Systems
