计算机视觉粒子流
Towards a Computer Vision Particle Flow
摘要：
在高能物理实验中，粒子流（PFlow）算法被设计为提供碰撞期间在检测器接受范围内产生的粒子的性质和运动学性质的最佳重建。PFlow算法的核心是能够区分中性粒子的热量计能量沉积与带电粒子的能量沉积，使用带电粒子跟踪设备的补充测量，以提供粒子含量和运动学的卓越测量。在本文中，一个计算机视觉的方法，这一基本方面的PFlow算法，热量计图像的基础上，提出。对最先进的深度学习技术进行了比较研究。一个显着改善重建的中性粒子量热能量存款获得的背景下，大的重叠与带电粒子的存款。使用超分辨率技术还获得了具有增强的更细粒度的量热计图像。
英文摘要：
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.
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