Searching new physics with advanced data analysis techniques

利用先进的数据分析技术寻找新的物理学

• 批准号: 1941809
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1941809
• 关键词: Searching; new; physics; advanced; data

Separating signal events from large Standard Model backgrounds is a highly challenging task at the Large Hadron Collider. The student will develop novel analysis techniques to perform this task. In recent years machine-learning methods have become increasingly important to exploit kinematic features in the separation between signal and background. Andrew will develop new techniques for unsupervised learning algorithms which allows to train these methods directly on data. He will then apply these methods to specific new physics models and assess the sensitivity in searching for such extensions of the Standard Model.

在大型强子对撞机上，从大型标准模型背景中分离信号事件是一项极具挑战性的任务。学生将开发新的分析技术来执行这项任务。近年来，机器学习方法在利用信号和背景之间的分离的运动学特征方面变得越来越重要。Andrew将开发用于无监督学习算法的新技术，允许直接在数据上训练这些方法。然后，他将把这些方法应用于特定的新物理模型，并评估在寻找标准模型的这种扩展时的灵敏度。

项目成果

Novel B -decay signatures of light scalars at high energy facilities

高能设施中光标量的新颖 B 衰变特征

• DOI: 10.1103/physrevd.100.115015
• 发表时间: 2019
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Blance A

Adversarially-trained autoencoders for robust unsupervised new physics searches

• DOI: 10.1007/jhep10(2019)047
• 发表时间: 2019-10-04
• 期刊: JOURNAL OF HIGH ENERGY PHYSICS
• 影响因子: 5.4
• 作者: Blance, Andrew;Spannowsky, Michael;Waite, Philip
• 通讯作者: Waite, Philip