| Issue |
EPJ Web Conf.
Volume 320, 2025
20th International Conference on Calorimetry in Particle Physics (CALOR 2024)
|
|
|---|---|---|
| Article Number | 00026 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/epjconf/202532000026 | |
| Published online | 07 March 2025 | |
https://doi.org/10.1051/epjconf/202532000026
Vertex Imaging Hadron Calorimetry Using AI/ML Tools
Advanced Particle Detector Laboratory, Department of Physics and Astronomy, Texas Tech University, Lubbock, TX, USA
* e-mail: shuichi.kunori@ttu.edu
Published online: 7 March 2025
The fluctuations in energy loss to processes that do not generate measurable signals, such as binding energy losses, set the limit on achievable hadronic energy resolution in traditional energy reconstruction techniques. The correlation between the number of hadronic interaction vertices in a shower and the invisible energy is found to be strong and is used to estimate the invisible energy fraction in highly granular calorimeters in short time intervals (<10 ns). We simulated images of hadronic showers using GEANT4 and deployed a neural network to analyze the images for energy regression. The neural network-based approach results in a significant improvement in energy resolution, from 13% to 4% in the case of a Cherenkov calorimeter for 100 GeV pion showers. We discuss the significance of the phenomena responsible for this improvement and the plans for experimental verification of these results and further development.
© The Authors, published by EDP Sciences, 2025
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