Jul 6 – 8, 2021
Europe/Zurich timezone

Machine learning based Particle Flow algorithm and application of super-resolution techniques

Jul 7, 2021, 2:00 PM


Sanmay Ganguly (Weizmann Institute of Science (IL))


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 presentation, 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.

Affiliation Weizmann Institute of Science
Academic Rank Postdoctoral researcher

Primary author

Sanmay Ganguly (Weizmann Institute of Science (IL))


Jonathan Shlomi (Weizmann Institute of Science (IL)) Eilam Gross (Weizmann Institute of Science (IL)) Marumi Kado (Sapienza Universita e INFN, Roma I (IT)) Lorenzo Santi (Sapienza Universita e INFN, Roma I (IT)) Francesco Armando Di Bello (Sapienza Universita e INFN, Roma I (IT))

Presentation materials