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Title: AI-assisted detector design for the EIC (AID(2)E)

Journal Article · · Journal of Instrumentation
 [1]; ORCiD logo [2];  [3];  [4];  [5];  [4];  [4];  [3];  [2];  [3];  [2];  [6];  [4];  [4]
  1. Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
  2. College of William and Mary, Williamsburg, VA (United States)
  3. Duke Univ., Durham, NC (United States)
  4. Brookhaven National Laboratory (BNL), Upton, NY (United States)
  5. The Catholic Univ. of America, Washington, DC (United States)
  6. Duke Univ., Durham, NC (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using Geant4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Nuclear Physics (NP)
Contributing Organization:
AID(2)E Collaboration
Grant/Contract Number:
SC0012704; SC0024625; SC0024478; AC05-06OR23177; SC0024691
OSTI ID:
2406888
Report Number(s):
BNL--225826-2024-JAAM
Journal Information:
Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 07 Vol. 19; ISSN 1748-0221
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English

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