CAREER: Applying New Tools to the Discovery and Measurement of the New Standard Model

职业:应用新工具来发现和衡量新标准模型

基本信息

  • 批准号:
    0955626
  • 负责人:
  • 金额:
    $ 40万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2010
  • 资助国家:
    美国
  • 起止时间:
    2010-07-01 至 2016-06-30
  • 项目状态:
    已结题

项目摘要

Experimental studies of proton collisions at the LHC are likely to reveal the answers to some of the most fundamental questions in physics. Already, experiments like Fermi GST and PAMELA are producing very exciting - though puzzling - results that may be major hints to an unexpectedly rich dark matter sector. There are many possibilities for dramatic discoveries, which will usher in a new era for dark matter and fundamental particle physics. How one combines the complementary information from these disparate measurements into a cohesive picture is not readily apparent. Initially, it is unlikely that an obvious picture will emerge that is more compelling than conventional explanations, such as poorly understood modeling or experimental systematics. Thus, one must be prepared to pose serious questions about the data, the theoretical interpretation, the experimental systematics, and additional modeling assumptions that are not limited by avoidable technical limitations. This award supports a program of physics research built around a unifying approach to large-scale scientific data analysis, initially dealing with data from LHC experiments, in a way that is scalable with respect to complexity of data, experimental apparatuses, and theoretical models. The approach could have profound impact on early standard model physics measurements and the extension to Higgs physics and beyond as well as facilitate communication and cooperation between ATLAS and CMS, between theorists and experimentalists, and among researchers from different disciplines. In terms of broader impact the key development in this award that enables the scalable approach to large-scale data analysis is applicable beyond the field of particle physics. Experiments as diverse as Fermi GST, HESS, and the IceCube neutrino telescope are already exploring the use of this technology, which is a concrete indication of the broader impacts of the proposed research. Additional impacts follow from a well-integrated educational and outreach component. A portable and professionally-designed exhibit will be constructed for use at the popular New York City World Science Festival street fair, area science fairs, career fairs, and the like. The booth will utilize existing material from the ATLAS outreach group. Additionally, the exhibit will be the basis for a project integrated into a teaching methods course in the Department of Teaching and Learning at NYU. The project will be to design activities for the exhibit itself and to develop and assess physics teaching methods. Thus, the exhibit, the activities, and the assessment of the exhibit will be the culmination of a semester-long project, and those teachers can bring the developed activities to their own schools. Finally, the PI will mentor high-school students from the Harlem Children's Society so that they can understand and operate the demonstrations, thus serving as peer-group liaisons, and role models for under-represented groups in the sciences.
在大型强子对撞机上进行的质子碰撞实验研究可能会揭示物理学中一些最基本问题的答案。像Fermi GST和PAMELA这样的实验已经产生了非常令人兴奋的-尽管令人困惑的-结果,这些结果可能是一个意想不到的丰富暗物质部门的主要暗示。戏剧性的发现有很多可能性,这将为暗物质和基本粒子物理学开创一个新时代。人们如何将这些不同测量的互补信息组合成一幅有凝聚力的画面并不容易看出。最初,不太可能出现一个比传统解释更令人信服的明显画面,例如理解不深的建模或实验系统学。因此,人们必须准备好对数据、理论解释、实验系统学和不受可避免的技术限制限制的其他建模假设提出严肃的问题。该奖项支持围绕大规模科学数据分析的统一方法建立的物理研究计划,最初处理LHC实验数据,在数据,实验设备和理论模型的复杂性方面具有可扩展性。该方法可能对早期标准模型物理测量和希格斯物理学的扩展产生深远的影响,并促进ATLAS和CMS之间,理论家和实验家之间以及来自不同学科的研究人员之间的交流与合作。就更广泛的影响而言,该奖项的关键发展使大规模数据分析的可扩展方法适用于粒子物理学领域之外。像费米GST、HESS和IceCube中微子望远镜这样的实验已经在探索这项技术的使用,这具体表明了拟议研究的更广泛影响。其他影响来自于教育和外联组成部分的良好整合。将建造一个便携式和专业设计的展览,用于受欢迎的纽约市世界科学节街头博览会、地区科学博览会、职业博览会等。展位将利用ATLAS外联小组的现有材料。此外,该展览将成为一个项目的基础,该项目将纳入纽约大学教学与学习系的教学方法课程。该项目将为展览本身设计活动,并制定和评估物理教学方法。因此,展览,活动和展览的评估将是一个学期的项目的高潮,这些教师可以把开发的活动带到自己的学校。最后,PI将指导来自哈莱姆儿童协会的高中生,使他们能够理解和操作演示,从而作为同龄人群体的联络人,并为科学领域代表性不足的群体树立榜样。

项目成果

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Kyle Cranmer其他文献

Improving inference with matrix elements and machine learning
利用矩阵元素和机器学习改进推理
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    J. Brehmer;Kyle Cranmer;Irina Espejo;F. Kling;Gilles Louppe;J. Pavez
  • 通讯作者:
    J. Pavez
Searching for new physics: Contributions to LEP and the LHC
寻找新物理学:对 LEP 和 LHC 的贡献
  • DOI:
  • 发表时间:
    2005
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kyle Cranmer
  • 通讯作者:
    Kyle Cranmer
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
任意时空维度中晶格规范理论的归一化流
  • DOI:
    10.48550/arxiv.2305.02402
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ryan Abbott;M. S. Albergo;Aleksandar Botev;D. Boyda;Kyle Cranmer;D. Hackett;G. Kanwar;A. G. Matthews;S. Racanière;Ali Razavi;Danilo Jimenez Rezende;F. Romero;P. Shanahan;Julian M. Urban
  • 通讯作者:
    Julian M. Urban
Likelihood-free inference with an improved cross-entropy estimator
使用改进的交叉熵估计器进行无似然推理
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Stoye;J. Brehmer;Gilles Louppe;J. Pavez;Kyle Cranmer
  • 通讯作者:
    Kyle Cranmer
BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btl655 Structural bioinformatics Biskit—A software platform for structural bioinformatics
生物信息学应用说明 doi:10.1093/bioinformatics/btl655 结构生物信息学 Biskit—结构生物信息学软件平台
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Goodman;A. Pepe;A. Blocker;C. Borgman;Kyle Cranmer;M. Crosas;R. D. Stefano;Yolanda Gil;Paul Groth;M. Hedstrom;D. Hogg;V. Kashyap;A. Mahabal;A. Siemiginowska;A. Slavkovic
  • 通讯作者:
    A. Slavkovic

Kyle Cranmer的其他文献

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{{ truncateString('Kyle Cranmer', 18)}}的其他基金

Collaborative Research: Disciplinary Improvements: FAIROS-HEP, a Research Coordination Network for Particle Physics
合作研究:学科改进:FAIROS-HEP,粒子物理学研究协调网络
  • 批准号:
    2226380
  • 财政年份:
    2022
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
Collaborative Research: Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN)
合作研究:用于人工智能和似然自由推理的可扩展网络基础设施 (SCAILFIN)
  • 批准号:
    1841471
  • 财政年份:
    2018
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
Collaborative Research: SI2-SSI: Data-Intensive Analysis for High Energy Physics (DIANA/HEP)
合作研究:SI2-SSI:高能物理数据密集型分析 (DIANA/HEP)
  • 批准号:
    1450310
  • 财政年份:
    2015
  • 资助金额:
    $ 40万
  • 项目类别:
    Continuing Grant

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Empowering Archivists: Applying New Tools and Approaches for Better Representation of Women in Audio-Visual Collections
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旨在打造抗菌涂层根面!
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Developing and Applying New Approaches to Air Pollution Exposure Measurement for Individuals and Populations
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Developing and applying a new PS-InSAR method to measure the nonlinear behavior of structures and infrastructure facilities
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