AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
arXiv:1911.05797 · doi:10.1088/1748-0221/15/05/P05009
Abstract
Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. We propose a general approach to this process based on Bayesian optimization and machine learning that encodes detector requirements. As a case study, we focus on the design of the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as part of the particle-identification system at the future Electron-Ion Collider (EIC). The EIC is a US-led frontier accelerator project for nuclear physics, which has been proposed to further explore the structure and interactions of nuclear matter at the scale of sea quarks and gluons. We show that the detector design obtained with our automated and highly parallelized framework outperforms the baseline dRICH design within the assumptions of the current model. Our approach can be applied to any detector R&D, provided that realistic simulations are available.
arXiv comment22 pages, 11 figures
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Cited by in corpus (10)
- Machine Learning in Nuclear Physics
- Precision Studies of QCD in the Low Energy Domain of the EIC
- The Present and Future of QCD
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- The Thick Gas Electron Multiplier and its derivatives: physics, technologies and applications
- AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider
- Performance optimization for a scintillating glass electromagnetic calorimeter at the EIC
- Automatic optimisation of a Parallel-Plate Avalanche Counter with Optical Readout
- EIC Detector Overview
- Design of Detectors at the Electron Ion Collider with Artificial Intelligence