Speaker
Dr
Alexey Boldyrev
(National Research University Higher School of Economics)
Description
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 optimization 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. Being combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase 2 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 occupancies and spatial resolution for the future LHCb Calorimeter setup at different pile-up conditions.
Primary author
Dr
Alexey Boldyrev
(National Research University Higher School of Economics)
Co-authors
Andrew Shevelev
(National Research University Higher School of Economics)
Dr
Denis Derkach
(National Research University Higher School of Economics)
Dr
Fedor Ratnikov
(Yandex School of Data Analysis)
Leonid Matyushin
(National Research University Higher School of Economics)
Pavel Fakanov
(National Research University Higher School of Economics)
