Design and Performance Studies of a Granular Thin HCAL-MuID Detector for the EIC Optimized for AI-Based Reconstruction
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916059974467584 |
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| author | Kelleher, Rowan Vossen, Anselm Jacobs, William W. Visser, Gerard Schneider, Simon Ilieva, Yordanka Nadel-Turonski, Pawel |
| author_facet | Kelleher, Rowan Vossen, Anselm Jacobs, William W. Visser, Gerard Schneider, Simon Ilieva, Yordanka Nadel-Turonski, Pawel |
| contents | We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent timing resolution, enabling time-of-flight capabilities as well as a more compact overall assembly. Machine learning has been integrated into the detector design process from the ground up. Detector design objectives are defined using Machine Learning based reconstruction and Machine Learning is used to optimize the detector design. The highly segmented readout is implemented with Machine Learning algorithms in mind to reach performance levels usually reserved for much more expensive detector systems. The primary physics objective is to serve as a muon detector/ID system and a neutral hadron calorimeter. In EIC kinematics, charged particles are best measured through tracking rather than calorimetry, but the hKLM can identify and measure the momentum of neutral hadrons. The latter are mainly $K_L$'s and neutrons: for lower energies, excellent relative momentum measurements of a few 10\% are achieved using time of flight, while for higher particle momenta, the energy can be measured calorimetrically with a resolution significantly better than that demonstrated for similar calorimeters read out with less granularity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_08432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design and Performance Studies of a Granular Thin HCAL-MuID Detector for the EIC Optimized for AI-Based Reconstruction Kelleher, Rowan Vossen, Anselm Jacobs, William W. Visser, Gerard Schneider, Simon Ilieva, Yordanka Nadel-Turonski, Pawel Instrumentation and Detectors High Energy Physics - Experiment We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent timing resolution, enabling time-of-flight capabilities as well as a more compact overall assembly. Machine learning has been integrated into the detector design process from the ground up. Detector design objectives are defined using Machine Learning based reconstruction and Machine Learning is used to optimize the detector design. The highly segmented readout is implemented with Machine Learning algorithms in mind to reach performance levels usually reserved for much more expensive detector systems. The primary physics objective is to serve as a muon detector/ID system and a neutral hadron calorimeter. In EIC kinematics, charged particles are best measured through tracking rather than calorimetry, but the hKLM can identify and measure the momentum of neutral hadrons. The latter are mainly $K_L$'s and neutrons: for lower energies, excellent relative momentum measurements of a few 10\% are achieved using time of flight, while for higher particle momenta, the energy can be measured calorimetrically with a resolution significantly better than that demonstrated for similar calorimeters read out with less granularity. |
| title | Design and Performance Studies of a Granular Thin HCAL-MuID Detector for the EIC Optimized for AI-Based Reconstruction |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2511.08432 |