Calorimetric Measurement of Multi-TeV Muons via Deep Regression

TL;DR

This paper proposes a novel calorimetric method using deep regression to measure ultra-TeV muon energies with high precision, addressing limitations of magnetic curvature-based techniques in high-energy particle physics experiments.

Contribution

It introduces a new deep learning-based approach for muon energy measurement using calorimeter data, achieving better than 20% resolution for ultra-TeV muons, which is a significant improvement.

Findings

Achieves <20% relative energy resolution for ultra-TeV muons.

Demonstrates feasibility of calorimetric measurement for high-energy muons.

Employs convolutional neural networks for spatial energy pattern analysis.

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.