Abstract
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.
Highlights
The calorimeters are an essential part of most of the existing and developing detectors in high energy physics
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
It is often possible to improve the physics performance of the calorimeter using advanced detector response and reconstruction techniques, including ones based on machine learning
Summary
The calorimeters are an essential part of most of the existing and developing detectors in high energy physics. The high luminosity delivered by the collider causes a high multiplicity and hit occupancy in the calorimeter. To operate in such conditions, a new generation of the calorimeters are being developed. It is often possible to improve the physics performance of the calorimeter using advanced detector response and reconstruction techniques, including ones based on machine learning. Processes of multi-parametric optimisation appear to be expensive These factors make new approaches to calorimeter development necessary. Three main steps can represent the pipeline: particle generation and propagation (I), detector response and reconstruction (II) and metric calculation (III)
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