Calorimetric Measurement of Multi-TeV Muons via Deep Regression

Abstract

The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momentum at very high energy, where an estimate of the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient for achieving good momentum resolution when detecting, e.g., a narrow, high mass resonance decaying to a muon pair. In this work we study the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by exploiting spatial information of the clusters of energy from radiated photons in a regression task. The use of a task-specific deep learning architecture based on convolutional layers allows us to treat the problem as one akin to image reconstruction, where images are constituted by the pattern of energy released in successive layers of the calorimeter. A measurement of muon energy with better than 20% relative resolution is shown to be achievable for ultra-TeV muons.

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References in corpus (19)

• The NumPy array: a structure for efficient numerical computation
• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Observation of the Top Quark
• The Physics of Heavy Z' Gauge Bosons
• Muon reconstruction performance of the ATLAS detector in proton--proton collision data at $\sqrt{s}$=13 TeV
• Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data
• Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC
• Energy Reconstruction Methods in the IceCube Neutrino Telescope
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• An improved method for measuring muon energy using the truncated mean of dE/dx
• Performance of the reconstruction and identification of high-momentum muons in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Hadronic energy resolution of a highly granular scintillator-steel hadron calorimeter using software compensation techniques
• Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
• Shower development of particles with momenta from 15 GeV to 150 GeV in the CALICE scintillator-tungsten hadronic calorimeter
• Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter
• Calorimetric Measurement of Multi-TeV Muons via Deep Regression
• Energy Reconstruction of Hadrons in highly granular combined ECAL and HCAL systems

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