Convolutional Neural Networks for event classification

被引：0
|
作者：
Rubio Jimenez, Adrian [1 ]
Garcia Navarro, Jose Enrique [1 ]
Moreno Llacer, Maria [1 ]
机构：
[1] UV, Inst Fis Corpuscular, Mixed Ctr, CSIC, Catedratico Jose Beltran 2, Valencia, Spain
来源：
NINTH ANNUAL CONFERENCE ON LARGE HADRON COLLIDER PHYSICS, LHCP2021 | 2021年
关键词：
D O I：
暂无
中图分类号：
O469 [凝聚态物理学];
学科分类号：
070205 ;
摘要：
Cutting-edge Artificial Intelligence is being implemented in a wide range of tasks in High Energy Physics (HEP) in order to facilitate the analysis of large datasets. However, visual recognition has not been explored as much in HEP for event classification. This study shows how Convolutional Neural Networks could be applied for such an important task, for which a novel method to represent the event information in images is explored. This technique is applied for a classification problem corresponding to a search for Dark Matter in proton-proton collisions. The results obtained with this technique are also compared with the performance of a Boosted Decision Tree.
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页数：5
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