Interaction networks for the identification of boosted H→bb¯ decays
Interaction networks for the identification of boosted H→bb¯ decays
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DOI：
10.1103/physrevd.102.012010
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发表时间：
2019-09
影响因子：
5
通讯作者：
Eric A. Moreno;Thong Q. Nguyen;J. Vlimant;O. Cerri;H. Newman;Avikar Periwal;M. Spiropulu;Javier Mauricio Duarte;M. Pierini
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文献类型：
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作者：
Eric A. Moreno;Thong Q. Nguyen;J. Vlimant;O. Cerri;H. Newman;Avikar Periwal;M. Spiropulu;Javier Mauricio Duarte;M. Pierini
我们开发了一个算法的基础上的相互作用网络，以确定高横动量希格斯玻色子衰变到底夸克-反夸克对，并区分它们从普通的喷流，反映在短距离的夸克和胶子的配置。该算法的输入是射流中重构带电粒子的特征以及与它们相关联的次顶点。将喷流簇射描述为粒子到粒子和粒子到顶点相互作用的组合，训练模型以学习优化分类问题的喷流表示。该算法是在现实LHC碰撞的模拟样本上训练的，由CMS协作组在CERN开放数据门户网站上发布。互动网络实现了一个显着的改善，在识别性能方面的国家的最先进的算法。
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm’s inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.