Investigations in High Energy Physics

拟议的项目将解决当前和未来粒子加速器，特别是欧洲核子研究中心的大型强子对撞机（LHC）的实验如何帮助我们理解粒子物理学标准模型，电弱对称性破缺和暗物质的问题。计划中的项目的一部分集中在理论模型上，这些模型可以同时解释电弱对称性破缺的规模和暗物质以弱相互作用大质量粒子形式的性质。将比较不同的模型及其潜在的实验特征，以开发独立于模型的分析技术。拟议的研究工作，虽然理论上的性质，将直接有利于实验LHC合作。该项目的另一部分侧重于重夸克，对撞机和引力物理学的有效场论应用。特别是开发一个有效的场论计算低x物理和使用有效场论的重力调查感兴趣的问题LIGO和丽莎。这个建议的更广泛的影响是，这个项目研究了几个基本问题，包括直接的现象学相关的问题，目前和未来几代的粒子加速器。所研究的问题将对这些设施的结果的解释产生直接影响。建议的项目非常适合不同层次的学生参与，让他们获得理论物理，数学方法，计算机编程和数据分析的知识和技能。

The proposed project will address the question of how experiments at current and future particle accelerators, in particular the Large Hadron Collider (LHC) at CERN, can help us understand the Standard Model of Particle Physics, electroweak symmetry breaking and dark matter. Part of the planned project focuses on theoretical models that can simultaneously explain the scale of electroweak symmetry breaking and the nature of dark matter in the form of weakly interacting massive particles. Different models and their potential experimental signatures will be compared, in order to develop model-independent analysis techniques. The proposed research work, while theoretical in nature, will be directly beneficial for the experimental LHC collaborations. The other part of the project focuses on effective field theory applications for heavy quark, collider, and gravitational physics. In particular developing an effective field theory for calculating low-x physics and the use of an effective field theory for gravity to investigate questions of interest to LIGO and LISA. The broader implications of this proposal are that this project examines several fundamental issues including questions of direct phenomenological relevance to present and future generations of particle accelerators. The issues studied will have a direct bearing on the interpretation of results from these facilities. The proposed project is well suited for the participation of students at different levels, allowing them to acquire knowledge and skills in theoretical physics, mathematical methods, computer programming and data analysis.