LHC新机器学习应用提升粒子物理分析效率与精度
Novel machine learning applications at the LHC
- 采用新型机器学习方法优化分类与快速模拟
- 实现更精准的信号分离与背景抑制
- 适合关注高能物理算法创新的研究者
机器学习（ML）是粒子物理领域迅速发展的研究方向，在欧洲核子研究中心（CERN）大型强子对撞机（LHC）中具有广泛应用。ML改变了粒子物理学家进行搜索和测量的方式，作为多功能工具提升了现有方法性能，并实现了根本性新方法。本文介绍了LHC实验中用于改进分类、快速模拟、去卷积和异常检测的新型机器学习技术及近期成果。
原文摘要 · Abstract (English)
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile tool used to improve existing approaches and enable fundamentally new ones. In these proceedings, we describe novel ML techniques and recent results for improved classification, fast simulation, unfolding, and anomaly detection in LHC experiments.
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