Heavy flavor identification at CMS with deep neural networks
Heavy flavor identification at CMS with deep neural networks
复制标题
利用深度神经网络在 CMS 中识别重口味
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Ebony M. Dill
中科院分区：
文献类型：
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作者：
S. L. Staggs;M. L. White;Paul A. Schewe;Erica B Davis;Ebony M. Dill
在大型强子对撞机中，识别源自重味夸克（b 或 c 标记）的喷流对于寻找新物理和标准模型过程的测量非常重要。 CMS 开发了多种 b 标记算法，根据带电粒子轨道的撞击参数、重建衰变顶点的特性、轻子是否存在或其组合等变量来选择 b 夸克喷流。这些算法严重依赖机器学习工具，因此是深度神经网络等高级工具的自然候选者。新算法 DeepCSV 使用深度神经网络。输入与现有 CSVv2 b 标记器使用的同一组可观察量，并扩展为使用更多曲目的信息。此外，还调整了训练策略，使用约 5000 万个喷射器来训练深度神经网络。新的 DeepCSV 算法优于 CSVv2 标记器，绝对 b 标记效率提高了约 4%，而淡味喷气机的误识别概率为 1%。此外，DeepCSV 是一个同时训练 c 标记的多分类器。对于 c 标记，DeepCSV 优于 CMS 中的其他标记器。
At the Large Hadron Collider, the identification of jets originating from heavy flavour quarks (b or c-tagging) is important for searches for new physics and for measurements of standard model processes. A variety of b-tagging algorithms has been developed by CMS to select b-quark jets based on variables such as the impact parameters of the charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton, or combinations thereof. These algorithms heavily rely on machine learning tools and are thus natural candidates for advanced tools like deep neural networks. A new algorithm, DeepCSV, uses a deep neural network. The input is the same set of observables used by the existing CSVv2 b-tagger, with the extension that it uses information of more tracks. Also, the training strategy was adapted and about 50 million jets are used for the training of the deep neural network. The new DeepCSV algorithm outperforms the CSVv2 tagger, with an absolute b-tagging efficiency improvement of about 4% for a misidentification probability for light-flavour jets of 1%. In addition, DeepCSV is a multiclassifier simultaneously trained for c-tagging. For c-tagging DeepCSV outperforms the other taggers in CMS.