AI-optimized detector design for the future Electron-Ion Collider: The dual-radiator RICH case

E. Cisbani, A. Del Dotto, C. Fanelli, M. Williams, M. Alfred, F. Barbosa, L. Barion, V. Berdnikov, W. Brooks, T. Cao, M. Contalbrigo, S. Danagoulian, A. Datta, M. Demarteau, A. Denisov, M. Diefenthaler, A. Durum, D. Fields, Y. Furletova, C. GleasonM. Grosse-Perdekamp, M. Hattawy, X. He, H. Van Hecke, D. Higinbotham, T. Horn, C. Hyde, Y. Ilieva, G. Kalicy, A. Kebede, B. Kim, M. Liu, J. McKisson, R. Mendez, P. Nadel-Turonski, I. Pegg, D. Romanov, M. Sarsour, C. L. Da Silva, J. Stevens, X. Sun, S. Syed, R. Towell, J. Xie, Z. W. Zhao, B. Zihlmann, C. Zorn

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numberP05009
JournalJournal of Instrumentation
Issue number5
StatePublished - May 2020


  • Cherenkov detectors
  • Detector design and construction technologies and materials

ASJC Scopus subject areas

  • Mathematical Physics
  • Instrumentation


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