Abstract
In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may improve the precision of the reconstruction methods being considered during detector R&D. Moreover, such reconstruction methods can be reproduced automatically while changing the main optimisation parameters of the detector like geometrical size, position, configuration, radiation length, Molière radius of the sensitive elements. This allows us to speed up the verification of the possible detector configurations and eventually the entire detector R&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector[1]. The reconstruction methods such as spatial reconstruction, timing reconstruction, and distinguishing of overlapped signals are covered in this paper.
Highlights
The calorimeters are an essential part of most of the existing and developing detectors in high energy physics
To obtain the planned physical performance during the R&D of modern experiments in HEP, the detailed Geant4 simulation[2] of the calorimeter is necessary. Such simulations are computationally expensive taking into account the large number of channels and the variety of possible options in the calorimeter module technologies, in the modules arrangement, in the reconstruction of the attributes of physical objects, etc
The recent efforts of based on Generative Adversarial Networks to the simulation of calorimeter showers prove them as good candidates for use in faster simulation in high energy particle physics [5,6,7]
Summary
The calorimeters are an essential part of most of the existing and developing detectors in high energy physics. To obtain the planned physical performance during the R&D of modern experiments in HEP, the detailed Geant simulation[2] of the calorimeter is necessary. Such simulations are computationally expensive taking into account the large number of channels and the variety of possible options in the calorimeter module technologies, in the modules arrangement, in the reconstruction of the attributes of physical objects, etc. Processes of multi-parametric optimisation appear to be expensive These factors make new approaches to calorimeter development necessary. The recent efforts of based on Generative Adversarial Networks to the simulation of calorimeter showers prove them as good candidates for use in faster simulation in high energy particle physics [5,6,7]. The paper, among other things, shows the applicability of the model based on machine learning reconstruction to shower inputs from several detector geometries
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