NSF-BSF: Elementary Particle Physics with ATLAS

NSF-BSF：使用 ATLAS 进行基本粒子物理

• 批准号: 2111244
• 负责人: Allen Mincer
• 金额: $ 173万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111244&HistoricalAwards=false
• 关键词: NSF; BSF; Elementary; Particle; Physics

This award will provide support for the NYU group working on the ATLAS experiment at the Large Hadron Collider (LHC) at CERN, a particle physics laboratory in Geneva, Switzerland. The LHC machine and ATLAS, a large particle detector facility, were built as basic science tools using funds from NSF and other agencies around the world. One of their primary objectives was to find the Higgs Boson, the last particle in the historically successful "Standard Model" (SM) that accounts for so much of the existence of, and forces between, known particles forming the matter in the universe. This effort has been successful. The next step in the experiments is to look for evidence for physics Beyond the Standard Model (BSM) that might, for instance, account for the presence of the mysterious "Dark Matter" that makes up so much of the mass of the universe. The LHC experiments are currently in the midst of analyzing data from Run 2, at almost twice the energy explored earlier and with significantly increased event samples. It is possible that evidence for BSM physics could emerge at this higher energy and with the higher event statistics from the current run and by means of further upgrades of the LHC and ATLAS to follow. The NYU group will be studying Higgs decays, searching for evidence of supersymmetry and exotic new states of matter through several approaches, including the reaction process known as Vector Boson Fusion, and will be developing novel approaches to the reconstruction and study of high energy Jets. NYU will partner with the Weizmann Institute of Science through support from the US-Israel Binational Science Foundation program to search for the decays of Higgs boson decays to charm quark pairs, through heavy flavor tagging. Here the joint NYU-Weizmann effort will exploit advanced machine learning techniques to identify and select such challenging final states in Higgs decay. Technical projects include improvements and refinements of the ATLAS trigger for missing energy, a key variable in the searches for new physics, and track triggering, an essential technique for dealing with reconstruction of events in very high rate collisions, which are endemic at the LHC. The group also helps to lead a smaller experimental effort called milliQan, whose objective is to search for fractionally charged particles that might be produced at the LHC. The group has a strong Machine Learning effort to further the precision of the Higgs measurements and to clarify BSM discovery in channels such as new Higgs particles in the so-called Hidden Valley suggested by string theory.The NYU group's broader impacts efforts are extensive, including a collaboration with the National Museum of Mathematics and Brookhaven National Laboratory designed to bring to the general public the excitement of particle physics and a better understanding of how a basic understanding of statistics can be used to assess conclusions that might be drawn from experiments or medical tests. The group's use of machine learning and simulation-based inference will continue to impact scientific areas beyond particle physics including neuroscience, epidemiology, and algorithmic fairness, and to foster collaboration between academia and industry. Additionally, the analytical projects of the group, which include statistical applications, machine learning, and analysis preservation tools and techniques, can be extended to fields outside of physics, and young researchers supported under this program will have training in such innovative and cross-cutting techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将为在瑞士日内瓦的粒子物理实验室CERN的ATLAS实验的NYU集团提供支持。 LHC机器和Atlas是一个大型粒子探测器设施，是使用NSF和世界其他机构的基金的基础科学工具建造的。他们的主要目标之一是找到Higgs Boson，这是历史上成功的“标准模型”（SM）中的最后一个粒子，该粒子构成了已知粒子在宇宙中形成物质的已知粒子之间的大部分和力。这项工作已经成功。实验的下一步是寻找超越标准模型（BSM）的物理学的证据，例如，这可能解释了构成宇宙质量大部分的神秘“暗物质”的存在。 LHC实验目前正在分析RUN 2的数据中，几乎是早期探索的能量的两倍，并且事件样本显着增加。 BSM物理学的证据可能会出现在这种较高的能量以及当前运行的较高的统计数据以及通过进一步升级LHC和Atlas的较高统计数据中。 NYU组将通过几种方法（包括称为Vector Boson Fusion的反应过程）来研究Higgs衰减，寻找超对称性和异国物质新状态的证据，并将开发新的重建方法和研究高能量的方法。纽约大学将通过美国 - 以色列双国科学基金会计划的支持与魏兹曼科学学院合作，以通过重型风味标记来搜索希格斯玻色子衰变，以魅力夸克对。在这里，NYU-WEIZMANN联合的工作将利用高级机器学习技术来识别并选择Higgs衰减中的这种具有挑战性的最终状态。技术项目包括对缺失能源的Atlas触发的改进和改进，这是搜索新物理学的关键变量以及跟踪触发，这是处理非常高速碰撞中事件的重建的必不可少的技术，在LHC中是特有的。该小组还有助于领导一个称为Milliqan的较小实验努力，其目的是搜索可能在LHC上产​​生的分馏粒子。 The group has a strong Machine Learning effort to further the precision of the Higgs measurements and to clarify BSM discovery in channels such as new Higgs particles in the so-called Hidden Valley suggested by string theory.The NYU group's broader impacts efforts are extensive, including a collaboration with the National Museum of Mathematics and Brookhaven National Laboratory designed to bring to the general public the excitement of particle physics and a better understanding of how a对统计数据的基本理解可用于评估可能从实验或医疗测试中得出的结论。该小组对机器学习和基于模拟的推理的使用将继续影响粒子物理以外的科学领域，包括神经科学，流行病学和算法公平，并促进学术界与行业之间的合作。此外，该小组的分析项目包括统计应用，机器学习和分析保护工具和技术，可以扩展到物理外的领域，该计划下支持的年轻研究人员将在此类创新和交叉切割技术方面进行培训。该奖项颁发了NSF的法定任务，并反映了通过评估概念的支持者的支持者，该奖项已被评估概念范围。

项目成果

Combination of searches for invisible decays of the Higgs boson using 139 fb−1 of proton-proton collision data at s=13 TeV collected with the ATLAS experiment

• DOI: 10.1016/j.physletb.2023.137963
• 发表时间: 2023-01
• 期刊: Physics Letters B
• 影响因子: 4.4
• 作者: Atlas Collaboration

Search for dark matter produced in association with a dark Higgs boson decaying into W+W− in the one-lepton final state at $$ \sqrt{s} $$ = 13 TeV using 139 fb−1 of pp collisions recorded with the ATLAS detector

使用 139 fbâ1 记录的 pp 碰撞，在 $$ sqrt{s} $$ = 13 TeV 处搜索与暗希格斯玻色子衰变成 W Wâ 的单轻子最终状态相关的暗物质。

• DOI: 10.1007/jhep07(2023)116
• 发表时间: 2023
• 期刊: Journal of High Energy Physics
• 影响因子: 5.4
• 作者: Aad, G.;Abbott, B.;Abbott, D. C.;Abeling, K.;Abidi, S. H.;Aboulhorma, A.;Abramowicz, H.;Abreu, H.;Abulaiti, Y.;Abusleme Hoffman, A. C.
• 通讯作者: Abusleme Hoffman, A. C.

Observation of electroweak production of two jets in association with an isolated photon and missing transverse momentum, and search for a Higgs boson decaying into invisible particles at 13 $$\text {TeV}$$ with the ATLAS detector

观察与孤立光子和缺失横向动量相关的两个射流的电弱产生，并使用 ATLAS 探测器寻找在 13 $$ ext {TeV}$$ 处衰变成不可见粒子的希格斯玻色子

• DOI: 10.1140/epjc/s10052-021-09878-z
• 发表时间: 2022
• 期刊: The European Physical Journal C
• 作者: Aad G

Search for heavy, long-lived, charged particles with large ionisation energy loss in pp collisions at $$ \sqrt{s} $$ = 13 TeV using the ATLAS experiment and the full Run 2 dataset

使用 ATLAS 实验和完整的 Run 2 数据集，在 $$ sqrt{s} $$ = 13 TeV 的 pp 碰撞中搜索具有大量电离能量损失的重、寿命长的带电粒子

• DOI: 10.1007/jhep06(2023)158
• 发表时间: 2023
• 期刊: Journal of High Energy Physics
• 影响因子: 5.4
• 作者: Aad, G.;Abbott, B.;Abbott, D. C.;Abed Abud, A.;Abeling, K.;Abhayasinghe, D. K.;Abidi, S. H.;Aboulhorma, A.;Abramowicz, H.;Abreu, H.
• 通讯作者: Abreu, H.

The Forward Physics Facility at the High-Luminosity LHC

• DOI: 10.1088/1361-6471/ac865e
• 发表时间: 2022-03
• 期刊: Journal of Physics G: Nuclear and Particle Physics
• 作者: Jonathan L. Feng;F. Kling;M. Reno;J. Rojo;D. Soldin;L. Anchordoqui;J. Boyd;A. Ismail;L. Harland–La