Maximising the capabilities of a jet classification algorithm on LHC track and vertex data

最大限度地发挥 LHC 轨迹和顶点数据上的喷气机分类算法的功能

• 批准号: 1966386
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1966386
• 关键词: Maximising; capabilities; jet; classification; algorithm

The Large Hadron collider (LHC) collides hydrogen nuclei 40 million times per second at the highest artificially available centre of mass energy. These collisions are recorded by large detectors like ATLAS with 100 million channels, creating Peta bytes of data every second. The ATLAS Inner Tracking Detector is by far the largest contributor to this channel count. The reconstruction of particle trajectories and the vertices where they intersect from the limited hit information of the Inner Detector is one of the greatest computational challenges of the LHC. Jets containing bottom hadrons (b-jets) have been a very important window into unexplored physics. The identification of b-jets is needed to observe the as of yet unmeasured largest decay channel of the Higgs boson into bottom-quark pairs. Heavy new TeV-scale resonances might couple preferably to third generation particles, like bottom quarks. Such resonances are of renewed interests as they can act as mediators between dark matter particles and normal matter. Restricting the parameter space of the mediators also provides constraints on models of dark matter particles. B-jets are identified through the decay properties of b-hadrons. The decay chain of b-hadrons always involves a weak decay. The resulting long lifetime has b-hadrons fly a distance of a few mm up to a few cm before they decay and leads to displaced secondary vertices. B-tagging uses the properties of reconstructed large impact parameter tracks and identified secondary and tertiary vertices to distinguish b-jets from jets originating from lighter quarks. An important criterium for the quality of b-tagging is the misidentification rate for non-b jets. When searching for a tiny signal in a large dataset dominated by background even a moderate mistag rate can be fatal. Machine learning and multivariate techniques such as neural nets and boosted decision trees are being used extensively in the identification of b-jets. An important aspect of such techniques is a careful preparation and selection of the input variables used. Reconstructing the underlying b-hadron decay topology provides an advantage . In the current ATLAS reconstruction this is done via the JetFitter algorithm, that tries to reconstruct a string of secondary and tertiary vertices along the jet direction. The project focuses on improving the b-jet identification especially under difficult conditions like large boost. This is done by investigating the known decay topologies and implementing broader options into JetFitter.

大型强子对撞机（LHC）以每秒4000万次的速度在最高的人工质量中心碰撞氢原子核。这些碰撞被ATLAS这样的大型探测器记录下来，它有1亿个通道，每秒产生1千万亿字节的数据。ATLAS内部跟踪探测器是迄今为止该通道数的最大贡献者。根据内部探测器的有限命中信息重建粒子轨迹及其相交的顶点是大型强子对撞机最大的计算挑战之一。含有底强子的喷流（b-jet）是探索未知物理学的一个非常重要的窗口。b-喷流的识别需要观察希格斯玻色子到底夸克对的最大衰变通道。重的新TeV尺度共振可能更适合耦合到第三代粒子，如底夸克。这种共振重新引起了人们的兴趣，因为它们可以充当暗物质粒子和正常物质之间的媒介。限制介质的参数空间也为暗物质粒子的模型提供了限制。b-喷注是通过b-强子的衰变性质来识别的。b强子的衰变链总是包含一个弱衰变。由此产生的长寿命使b-强子在衰变之前飞行几毫米到几厘米的距离，并导致次级顶点移位。b标记使用重建的大的影响参数轨道和确定的第二和第三顶点的属性，以区分b喷流从喷流起源于较轻的夸克。b标记质量的一个重要标准是对非b喷流的错误识别率。当在由背景主导的大型数据集中搜索微小信号时，即使是中等的错误率也可能是致命的。机器学习和多变量技术，如神经网络和提升决策树，正被广泛用于识别b-jet。这种技术的一个重要方面是仔细准备和选择所使用的输入变量。重建基本的b-强子衰变拓扑提供了一个优势。在当前的ATLAS重建中，这是通过JetFitter算法完成的，该算法试图沿着射流方向重建一串二级和三级顶点沿着。该项目的重点是改善b-jet识别，特别是在困难的条件下，如大助推。这是通过研究已知的衰减拓扑并在JetFitter中实现更广泛的选项来完成的。