2020/05/15 by Alexey Boldyrev, A. S. Boldyrev, Boldyrev, Alexey +8 · 1 citation
Engineering · Physics and Astronomy · #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Superconducting Materials and Applications #physics.ins-det
paper · pdf · doi:10.48550/arxiv.2005.07700
8 pages. arXiv admin note: text overlap with arXiv:2003.05118
arxiv created 2020/05/15 · openalex publication_date 2020/05/15 · arxiv updated 2020/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and background conditions, tunable reconstruction algorithms, and desired physics performance metrics. While combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase II Upgrade of the LHCb Calorimeter under the requirements on operation at high luminosity. We discuss the general design of the approach and particular estimations, including spatial and energy resolution for the future LHCb Calorimeter setup at different pile-up conditions.