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

大型强子对撞机(LHC)以每秒4000万次的速度在人工可用的最高质心处对撞氢核。这些碰撞由拥有1亿个通道的ATLAS这样的大型探测器记录下来，每秒产生Peta字节的数据。到目前为止，ATLAS内部跟踪探测器是该通道计数的最大贡献者。从内探测器有限的命中信息重建粒子轨迹和它们相交的顶点是大型强子对撞机最大的计算挑战之一。含有底部强子的喷注(b-喷注)一直是了解未知物理的一个非常重要的窗口。为了观察希格斯玻色子到底夸克对的最大衰变通道，需要对b喷注进行识别。重的新TeV尺度的共振可能会更好地耦合到第三代粒子，比如底夸克。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调解人。限制介体的参数空间也提供了对暗物质粒子模型的约束。B-喷注是通过b-强子的衰变性质来识别的。B强子的衰减链总是涉及到弱衰变。由此产生的长寿命使得b强子在衰变之前飞行了几毫米到几厘米的距离，并导致第二级顶点移位。B标记使用重建的大碰撞参数轨迹和识别的第二和第三顶点的特性来区分b喷流和源自较轻夸克的喷流。衡量b型喷嘴质量的一个重要标准是非b型喷嘴的误识率。在以背景为主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，如神经网络和增强决策树，正被广泛用于识别b型喷气式飞机。这类技术的一个重要方面是仔细准备和选择所用的输入变量。重建基本的b-强子衰变拓扑提供了一个优势。在目前的ATLAS重建中，这是通过JetFitter算法完成的，该算法试图重建沿喷流方向的一串二级和三级顶点。该项目的重点是改进b喷气式飞机的识别，特别是在大推力等困难条件下。这是通过调查已知的衰减拓扑并在JetFitter中实现更广泛的选项来实现的。

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.