Hidden Particles in Weak Scale Physics and Beyond
Hidden Particles in Weak Scale Physics and Beyond
批准号:
1415548
负责人:
Matthew Reece
金额:
$14.93万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-07-01 至 2017-06-30
中文摘要
该奖项资助了哈佛大学的马修·里斯教授的活动。粒子物理学的标准模型是在近40年前发展起来的,它解释了距离小到质子千分之一大小的绝大多数实验数据。然而,它留下了许多谜团。为什么两个基本粒子之间的引力比其他力弱得多?在我们的宇宙中,暗物质比正常物质普遍得多,但对光透明,迄今为止只能通过其引力效应来检测,这是什么?本研究旨在为探索这些和其他问题的各种实验项目提供理论支持。它将提出分析数据和从大型强子对撞机实验中存在的巨大背景中提取信号的新策略。这项研究还将建立新的模型,突出当前实验搜索可能错过的发现机会。它还将研究新的实验结果对自然性等重要范式的影响,这些范式塑造了理论物理学,但仍然需要与实验对抗。这一提议的更广泛影响将包括作为当地高中物理教师的资源,并帮助向年轻学生传达物理学的最新发现。通过与现有的,成功的外展计划物理理论网,这已经建立了与波士顿地区的高中连接,PI将访问当地的高中回答学生的问题,解释理论和实验之间的相互作用,进入像希格斯发现的发展,并帮助学生了解他们在科学领域的职业选择。该奖项支持的研究将在几个相互关联的领域进行,方向它将评估自然超对称理论的场景状态,包括PI开发的称为隐形超对称的模型。这将通过模拟这些模型对现有LHC搜索的预测,以及通过规划新的重点搜索如何更好地测试这些模型来实现。它还将评估宇宙学和暗物质在微调超对称模型中的地位,并更好地理解R-宇称违反如何在低尺度再加热温度的情况下避免暗物质的过度生产。这项研究将有助于确保LHC实验不会忽视可能的长寿命粒子,这可能揭示新的更高尺度的存在,以激励未来的实验。它还将研究来自空间的暗物质的可能间接探测信号与可在高精度地面实验中探测的隐藏扇区物理之间的联系。PI还将研究所有这些主题的量子场论方面。通过开展广泛的研究计划,PI将提供理论工具,以最大限度地发挥我们非常令人兴奋的,数据丰富的时代的影响。
英文摘要
This award funds the activities of Professor Matthew Reece at Harvard University.The Standard Model of particle physics was developed nearly forty years ago and explains the vast majority of experimental data at distances as small as one one-thousandth the size of a proton. Nonetheless, it leaves behind many mysteries. Why is the gravitational force between two fundamental particles vastly weaker than the other forces? What is the dark matter that is far more prevalent in our universe than normal matter, but transparent to light and so far detected only through its gravitational effects? This research aims to provide theoretical support to the diverse experimental programs that are probing these and other questions. It will propose new strategies for analyzing data and extracting signals from the huge backgrounds present at the Large Hadron Collider experiments. This research will also build new models that will highlight discovery opportunities that current experimental searches may be missing. It will also study the implications of new experimental results for important paradigms like naturalness, which have shaped theoretical physics but still require a confrontation with experiment. The broader impact of this proposal will be include serving as a resource for local high school physics teachers and helping to communicate recent discoveries in physics to young students. By working with the existing, successful outreach program Physics Theorynet, which has built connections with Boston-area high schools, the PI will visit local high schools to answer student questions, explain the interplay between theory and experiment that goes into developments like the Higgs discoveries, and help students understand the career options available to them in science.The research supported by this award will proceed in several interconnected directions. It will assess the status of scenarios for natural supersymmetric theories, including the class of models called Stealth Supersymmetry developed by the PI. This will be done through simulation of the predictions of these models for existing LHC searches and through planning of how new, focused searches could better test these models. It will also assess the status of cosmology and dark matter in fine-tuned supersymmetric models, and develop a better understanding of how R-parity violation could evade overproduction of dark matter in scenarios with low-scale reheating temperatures. The research will help to ensure that the LHC experiments do not overlook possible long-lived particles, which could reveal the existence of new, higher scales to motivate future experiments. It will also study the connection between possible indirect detection signals of dark matter from space and hidden-sector physics that can be probed in high-precision terrestrial experiments. The PI will also study aspects of quantum field theory underlying all of these topics. By working on a broad research program, the PI will provide theoretical tools to maximize the impact of our very exciting, data-rich era.
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