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

Cisbani, E.; Dotto, Alessio Del; Fanelli, C.; Williams, M.; Alfred, M.; Barbosa, Fernando; Barion, L.; Бердников, В. В.; Cao, T.; Contalbrigo, M.; Danagoulian, S.; Datta, A.; Demarteau, M.; Denisov, A.; Diefenthaler, M.; Durum, A.; Fields, D. E.; Furletova, Y.; Gleason, C.; Grosse‐Perdekamp, M.; Hattawy, M.; He, X.; Hecke, H. Van; Higinbotham, D. W.; Horn, T.; Hyde, Charles; Ilieva, Y.; Kalicy, G.; Kebede, A.; Kim, B.; Liu, M.; McKisson, J.; Méndez, Rodrigo Romero; Nadel-Turoński, P.; Pegg, Ian L.; Romanov, Dmitry; Sarsour, M.; Silva, C. L. da; Stevens, J. R.; Sun, Xudong; Syed, S.; Towell, R. S.; Xie, Junqi; Zhao, Z.; Zihlmann, B.; Zorn, C.

doi:10.1088/1748-0221/15/05/p05009

Cited by 30 publications

(38 citation statements)

References 26 publications

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“…The dRICH foresees C 2 F 6 as gas radiator (n 1.0008) and an aerogel radiator (n 1.02) for 3σ π/K separation in the range 3-60 GeV/c and efficient e/π separation from few hundred MeV up to ∼ 15 GeV/c in the EIC spectrometer forward region. The design has been supported with a complete Geant4 [279] simulation study and extensively using AI techniques with the Bayesian optimization to determine the optimal design parameters [280]. Figure 37 shows a typical simulated event in the dual RICH.…”

Section: Eic: Perspective Of Pid Techniques At the Electron-ion Collidermentioning

confidence: 99%

“…Multiple mirror panels (gray) focus rings from both aerogel and C 2 F 6 per-fluorocarbon radiators onto the same focal plane (GEANT4 simulation). Adapted from [280] which are being used to test the initial RICH prototypes, are not suitable for the setup at EIC because they should operate in a strong magnetic field. SiPMs as photon sensors make an appealing alternative, even if more tests are needed to establish the procedures to keep under control the noise rate, especially after irradiation.…”

Section: Eic: Perspective Of Pid Techniques At the Electron-ion Collidermentioning

confidence: 99%

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Trends in particle and nuclei identification techniques in nuclear physics experiments

et al. 2022

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Particle identification techniques are fundamental tools in nuclear physics experiments. Discriminating particles or nuclei produced in nuclear interactions allows to better understand the underlying physics mechanisms. The energy interval of these reactions is very broad, from sub-eV up to TeV. For this reason, many different identification approaches have been developed, often combining two or more observables. This paper reviews several of these techniques with emphasis on the expertise gained within the current nuclear physics scientific program of the Italian Istituto Nazionale di Fisica Nucleare (INFN).

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Section: Eic: Perspective Of Pid Techniques At the Electron-ion Collidermentioning

confidence: 99%

Section: Eic: Perspective Of Pid Techniques At the Electron-ion Collidermentioning

confidence: 99%

Trends in particle and nuclei identification techniques in nuclear physics experiments

et al. 2022

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“…A complete database of the various detector concepts for ATHENA, CORE and ECCE detectors can be found online [9]. a dual ring imaging Cherenkov detector (dRICH) [6], modular RICH [7], and detection of internally reflected Cherenkov light (DIRC) detectors [8], and electromagnetic and hadronic calorimeters. The dRICH detector in particular, has already made use of machine learning techniques in its design [6] and a lot of effort has gone into optimizing the tracking detectors sizes, especially for the lower field BaBar magnet configuration, making use of modern machine learning techniques.…”

Section: First Interaction Regionmentioning

confidence: 99%

EIC Detector Overview

Higinbotham

2022

Preprint

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The Electron Ion Collider will have two interaction regions that can be instrumented with detectors. The first region will be instrumented as part of the project and needs to be capable of delivering the physics that has been outlined by the National Academy of Sciences and ready at the start of beam commissioning near the end of this decade. Plans for a second complementary detector to be located at a second interaction region are already in progress and will hopefully come to fruition just a few years after the first detector comes online. While the basic parameters of these detectors are being selected using conventional approaches, the optimization of the detectors is already being enhanced by making use of advanced optimization techniques.

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“…The dotted red lines correspond to the projections on each variable of the optimal point found by the BO. More details can be found in [29].…”

Section: Detector Designmentioning

confidence: 99%

Machine learning for imaging Cherenkov detectors

Fanelli¹

2020

J. Inst.

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A : Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at present we are witnessing the onset of new highly efficient and fast applications. This paper focuses on novel directions with applications to Cherenkov detectors. In particular, recent advances on detector design and calibration, as well as particle identification are presented.

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

Cited by 30 publications

References 26 publications

Trends in particle and nuclei identification techniques in nuclear physics experiments

Trends in particle and nuclei identification techniques in nuclear physics experiments

EIC Detector Overview

Machine learning for imaging Cherenkov detectors

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