Towards a computer vision particle flow

Francesco Armando Di Bello, Sanmay Ganguly*, Eilam Gross, Marumi Kado, Michael Pitt, Lorenzo Santi, Jonathan Shlomi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number107
JournalEuropean Physical Journal C
Issue number2
StatePublished - Feb 2021
Externally publishedYes


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