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Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022)最新文献

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Sensitivity of DUNE to low energy physics searches DUNE对低能物理搜索的灵敏度
C. Cuesta
The Deep Underground Neutrino Experiment (DUNE), a next-generation long-baseline neutrino oscillation experiment, is a powerful tool to perform low energy physics searches. DUNE will be uniquely sensitive to the electron-neutrino-flavour component of the burst of neutrinos expected from the next Galactic core-collapse supernova, and also capable of detecting solar neutrinos. DUNE will have four modules of 70-kton liquid argon mass in total, placed 1.5 km underground at the Sanford Underground Research Facility in the USA. These modules are being designed exploiting different liquid argon time projection chamber technologies and based on the physics requirements that take into account the particularities of the low energy physics searches.
深地下中微子实验(DUNE)是下一代长基线中微子振荡实验,是进行低能物理搜索的强大工具。DUNE将对来自下一个银河系核心坍缩超新星的中微子爆发的电子中微子味成分具有独特的敏感性,并且还能够探测到太阳中微子。DUNE将有四个总质量为70万吨液态氩的模块,放置在美国桑福德地下研究设施地下1.5公里处。这些模块的设计利用了不同的液态氩气时间投影室技术,并基于考虑到低能物理搜索的特殊性的物理要求。
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引用次数: 1
Longevity studies for the CMS Muon System towards HL-LHC CMS μ子系统对HL-LHC的寿命研究
F. Primavera
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引用次数: 0
A Novel Algorithm to Reconstruct Events in a Water Cherenkov Detector 水切伦科夫检测器中事件重构的新算法
C. Yanagisawa
We have developed a novel approach to reconstruct events detected by a water-based Cherenkov detector such as Super- and Hyper-Kamiokande using an innovative deep learning algorithm. The algorithm is based on a generative neural network whose parameters are obtained by minimizing a loss function. In the training process with simulated single-particle events, the generative neural network is given the particle identification (ID) or type, 3d-momentum (p), and 3d-vertex position (V) as the inputs for each training event. Then the network generates a Cherenkov event that is compared with the corresponding true simulated event. Once the training is done, for the given Cherenkov event the algorithm will provide the best estimate on ID, p, and V by minimizing the loss function between the given event and the generated event over ranges of input values of ID, p and V. The algorithm serves as a type of fast simulation for a water Cherenkov detector with a fewer number of assumptions than traditional reconstruction methods. We will show some of the algorithm’s excellent performance in addition of the architecture and principle of the network
我们开发了一种新的方法来重建由水基切伦科夫探测器(如Super-和Hyper-Kamiokande)检测到的事件,使用创新的深度学习算法。该算法基于生成式神经网络,其参数通过最小化损失函数获得。在模拟单粒子事件的训练过程中,生成式神经网络将粒子识别(ID)或类型、三维动量(p)和三维顶点位置(V)作为每个训练事件的输入。然后网络生成一个Cherenkov事件,并与对应的真实模拟事件进行比较。一旦训练完成,对于给定的切伦科夫事件,该算法将通过最小化给定事件与生成事件之间的损失函数,在ID、p和V的输入值范围内,提供对ID、p和V的最佳估计。该算法作为水切伦科夫探测器的一种快速模拟,比传统重建方法的假设数量更少。除了网络的结构和原理外,我们还将展示该算法的一些优秀性能
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引用次数: 0
Probing new physics using Standard Model Effective Field Theory 用标准模型有效场论探索新物理学
A. Anisha
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引用次数: 0
A PYTHIA-8 underlying event tune from RHIC to the LHC 从RHIC到LHC的PYTHIA-8底层事件调优
I. Mooney, M. Rosales Aguilar, Z. Chang, R. Kunnawalkam Elayavalli, R. Fatémi, Yang He, Yuanjing Ji, D. Kalinkin, M. Kelsey, V. Verkest
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引用次数: 0
LCDAs of heavy hadrons and their first inverse moments 重强子的LCDAs及其第一逆矩
A. Shukhtina
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引用次数: 0
The Veto System of the JUNO Experiment 朱诺实验的否决系统
E. Baussan
{"title":"The Veto System of the JUNO Experiment","authors":"E. Baussan","doi":"10.22323/1.414.1117","DOIUrl":"https://doi.org/10.22323/1.414.1117","url":null,"abstract":"","PeriodicalId":286451,"journal":{"name":"Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114551525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Higgs Physics at a Muon Collider with detailed detector simulation μ子对撞机中的希格斯物理与详细的探测器模拟
L. Sestini, L. Buonincontri, M. Casarsa, L. Castelli, L. Giambastiani, A. Gianelle, D. Lucchesi, K. Krizka, Leo Mareso, A. Montella, D. Zuliani, S. Pagan Griso
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引用次数: 0
Hadron physics results at KLOE-2 KLOE-2强子物理结果
P. Gauzzi
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引用次数: 1
A measurement of the ratio of light-lepton inclusive semileptonic (B) decay rates (R(X_{e/mu})) = (mathcal{B}(B rightarrow X e nu)) / (mathcal{B}(B rightarrow X mu nu) ) using (189 mathrm{fb}^{-1}) of data at Belle II 利用贝尔2号的(189 mathrm{fb}^{-1})数据测量轻子包含半轻子的比率(B)衰变率(R(X_{e/mu})) = (mathcal{B}(B rightarrow X e nu)) / (mathcal{B}(B rightarrow X mu nu) )
H. Junkerkalefeld
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引用次数: 0
期刊
Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022)
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