Towards a computer vision particle flow

• Published in: The European physical journal. C, Particles and fields Vol. 81; no. 2; pp. 1 - 14
• Main Authors: Di Bello, Francesco Armando, Ganguly, Sanmay, Gross, Eilam, Kado, Marumi, Pitt, Michael, Santi, Lorenzo, Shlomi, Jonathan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021; Springer; Springer Nature B.V; Springer Verlag (Germany); SpringerOpen
• Subjects: Algorithms; Astronomy; Astrophysics and Cosmology; Charged particles; Comparative studies; Computer vision; Data Analysis, Statistics and Probability; Deposits; Detectors; Elementary Particles; Hadrons; Heavy Ions; High Energy Physics - Experiment; High Energy Physics - Phenomenology; Image reconstruction; Instrumentation and Detectors; Kinematics; Machine learning; Machine vision; Measurement Science and Instrumentation; Neutral particles; Nuclear Energy; Nuclear Physics; Particle tracking; Physics; Physics and Astronomy; Quantum Field Theories; Quantum Field Theory; Regular Article – Experimental Physics; State-of-the-art reviews; String Theory; Tracking devices; computer; numerical calculations; jet: energy resolution; calorimeter: cluster; spatial resolution; particle flow; charged particle: tracks; neural network
• ISSN: 1434-6044; 1434-6052
• DOI: 10.1140/epjc/s10052-021-08897-0

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics. In this paper, a computer vision approach to this fundamental aspect of PFlow algorithms, based on calorimeter images, is proposed. A comparative study of the state of the art deep learning techniques is performed. A significantly improved reconstruction of the neutral particle calorimeter energy deposits is obtained in a context of large overlaps with the deposits from charged particles. Calorimeter images with augmented finer granularity are also obtained using super-resolution techniques.