0


基于支持向量机的希格斯玻色子探测触发机制

Usage of SVM for a Triggering Mechanism for Higgs Boson Detection
课程网址: https://videolectures.net/videos/sikdd2017_kenda_higgs_boson  
主讲教师: Klemen Kenda
开课单位: 信息不详。欢迎您在右侧留言补充。
开课时间: 2017-12-08
课程语种: 英语
中文简介:
高能物理事件的实时分类对于处理CERN大型强子对撞机ATLAS探测器中质子-质子碰撞产生的大量数据至关重要。通过这项工作，我们实现了一种基于机器学习的触发机制方法来保存相关数据。与最先进的机器学习方法（梯度增强和深度神经网络）相比，支持向量机（SVM）的缺点已经被广泛的特征工程所弥补。该方法已经用领域专家提出的特殊指标（平均中位数显著性）进行了评估。与目前ATLAS探测器（XGBoost）上使用的最先进的方法相比，我们的方法具有更高的精度和8%的平均中位数显著性。
课程简介: Real-time classification of events in high energy physics is essential to deal with huge amounts of data, produced by proton-proton collisions in ATLAS detector at Large Hadron Collider in CERN. With this work we have implemented a triggering mechanism method for saving relevant data, based on machine learning. In comparison with the state of the art machine learning methods (gradient boosting and deep neural networks) shortcomings of Support Vector Machines (SVM) have been compensated with extensive feature engineering. Method has been evaluated with special metrics (average median significance) suggested by the domain experts. Our method achieves significantly higher precision and 8% lower average median significance than the current state of the art method used at ATLAS detector (XGBoost).
关 键 词: 质子碰撞; 机器学习; 探测器
课程来源: 视频讲座网
数据采集: 2025-05-09：zsp
最后编审: 2025-05-09：zsp
阅读次数: 2

纠错/补充/评论/留言
评论/留言 纠错/补充
验证算术题： 6 + 6 =