摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx记者来自悉尼大学的新闻报道，研究表明，“位于SuperKEKB能量不对称e+e-对撞机的Belle II实验于2019年开始运行。”记者们从西尼大学的研究中得到一句话：“从那时起，它记录了其前身收集的一半数据，并达到了4.7×1034cm-2s-1的瞬时光度世界纪录。对于Belle II背景事件中能量缺失的衰变，电磁量热计测量的能量是一个重要的量。”在计算剩余量热计能量时，应排除由束背景和假pH oton引起的量热计簇，因此在分析过程中识别它们是关键。本文提出了两种新的增强决策树分类器，它们经过训练，可以在Be LE II识别这些簇，并将它们与来自相互作用点碰撞事件的真实光子区分开来。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from the Universi ty of Sydney by NewsRx correspondents, research stated, “The Belle II experiment situated at the SuperKEKB energyasymmetric e+ e- collider began operation in 20 19.” The news correspondents obtained a quote from the research from University of Sy dney: “It has since recorded half of the data collected by its predecessor, and reached a world record instantaneous luminosity of 4.7 x 1034 cm-2s-1. For disti nguishing decays with missing energy from background events at Belle II, the res idual calorimeter energy measured by the electromagnetic calorimeter is an impor tant quantity. Ideally, calorimeter clusters due to beam backgrounds and fake ph otons should be excluded when the residual calorimeter energy is calculated, so identifying them during the analysis process is key. We present two new boosted decision tree classifiers that have been trained to identify such clusters at Be lle II and distinguish them from real photons originating from collision events at the interaction point.”
NETL
NSTL