Towards a Computer Vision Particle Flow
This work addresses a specific bottleneck in particle reconstruction for High Energy Physics experiments, offering an incremental improvement over existing methods.
The paper tackled the problem of distinguishing neutral from charged particle energy deposits in Particle Flow algorithms for High Energy Physics, using computer vision on calorimeter images, and achieved significantly improved reconstruction of neutral particle deposits, especially in overlapping scenarios.
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.