Using machine learning to speed up new and upgrade detector studies: a calorimeter case

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.