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1811070710726000640
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MIT
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© 2020 IOP Publishing Ltd and Sissa Medialab. 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 a potential component 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.
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2024-09-23T08:40:21Z
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Article
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mit-1721.1/134167
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Massachusetts Institute of Technology
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| language |
English
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2024-09-23T08:40:21Z
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| publishDate |
2021
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| publisher |
IOP Publishing
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dspace
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| spelling |
mit-1721.1/1341672022-04-01T17:27:46Z AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case © 2020 IOP Publishing Ltd and Sissa Medialab. 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 a potential component 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. 2021-10-27T19:58:27Z 2021-10-27T19:58:27Z 2020 2020-11-18T16:59:02Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134167 en 10.1088/1748-0221/15/05/P05009 Journal of Instrumentation Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing arXiv
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| spellingShingle |
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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| title |
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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| title_full |
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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| title_fullStr |
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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| title_full_unstemmed |
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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| title_short |
AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
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| title_sort |
ai optimized detector design for the future electron ion collider the dual radiator rich case
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| url |
https://hdl.handle.net/1721.1/134167
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