Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.

翻译：在超级粲-τ工厂实验中的粒子识别将由聚焦气凝胶环成像切伦科夫探测器（FARICH）提供。探测器位置的特殊性使得正常冷却变得困难，因此会有大量环境本底信号被捕获。必须抑制这些本底以降低数据流量并提高粒子速度分辨率。本文提出几种受计算机视觉机器学习技术启发的信号甄别方法。