Zhipeng Yao, Xingtao Huang, Teng Li, Weidong Li, Tao Lin, Jiaheng Zou
In collider physics experiments, particle identification (PID), i. e. the identification of the charged particle species in the detector is usually one of the most crucial tools in data analysis. In the past decade, machine learning techniques have gradually become one of the mainstream methods in PID, usually providing superior discrimination power compared to classical algorithms. In recent years, quantum machine learning (QML) has bridged the traditional machine learning and the quantum computing techniques, providing further improvement potential for traditional machine learning models. In this work, targeting at the $mu^{pm} /pi^{pm}$ discrimination problem at the BESIII experiment, we developed a variational quantum classifier (VQC) with nine qubits. Using the IBM quantum simulator, we studied various encoding circuits and variational ansatzes to explore their performance. Classical optimizers are able to minimize the loss function in quantum-classical hybrid models effectively. A comparison of VQC with the traditional multiple layer perception neural network reveals they perform similarly on the same datasets. This illustrates the feasibility to apply quantum machine learning to data analysis in collider physics experiments in the future.
在对撞机物理实验中，粒子识别（PID），即识别探测器中的带电粒子种类通常是数据分析中最关键的工具之一。近十年来，机器学习技术逐渐成为粒子识别的主流方法之一，与经典算法相比，机器学习技术通常具有更强的识别能力。近年来，量子机器学习（QML）在传统机器学习和量子计算技术之间架起了一座桥梁，为传统机器学习模型提供了进一步改进的潜力。在这项工作中，针对$mu^{pm} /pi^{pm/pi^{pm}$的辨别问题，我们开发了一种具有九个量子比特的变分量子分类器（VQC）。利用 IBM 量子模拟器，我们研究了各种编码电路和变分算法，以探索它们的性能。经典优化器能够有效地最小化量子经典混合模型中的损失函数。我们将 VQC 与传统的多层感知神经网络进行了比较，发现它们在相同数据集上的表现相似，这说明未来将量子机器学习应用于对撞机物理实验的数据分析是可行的。
{"title":"Muon/Pion Identification at BESIII based on Variational Quantum Classifier","authors":"Zhipeng Yao, Xingtao Huang, Teng Li, Weidong Li, Tao Lin, Jiaheng Zou","doi":"arxiv-2408.13812","DOIUrl":"https://doi.org/arxiv-2408.13812","url":null,"abstract":"In collider physics experiments, particle identification (PID), i. e. the\u0000identification of the charged particle species in the detector is usually one\u0000of the most crucial tools in data analysis. In the past decade, machine\u0000learning techniques have gradually become one of the mainstream methods in PID,\u0000usually providing superior discrimination power compared to classical\u0000algorithms. In recent years, quantum machine learning (QML) has bridged the\u0000traditional machine learning and the quantum computing techniques, providing\u0000further improvement potential for traditional machine learning models. In this\u0000work, targeting at the $mu^{pm} /pi^{pm}$ discrimination problem at the\u0000BESIII experiment, we developed a variational quantum classifier (VQC) with\u0000nine qubits. Using the IBM quantum simulator, we studied various encoding\u0000circuits and variational ansatzes to explore their performance. Classical\u0000optimizers are able to minimize the loss function in quantum-classical hybrid\u0000models effectively. A comparison of VQC with the traditional multiple layer\u0000perception neural network reveals they perform similarly on the same datasets.\u0000This illustrates the feasibility to apply quantum machine learning to data\u0000analysis in collider physics experiments in the future.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201430","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}
Natalia Szczepanek, David Britton, Alessandro Di Girolamo, Ewoud Ketele, Ivan Glushkov, Domenico Giordano, Ladislav Ondris, Emanuele Simili, Gonzalo Menendez Borge
As the scientific community continues to push the boundaries of computing capabilities, there is a growing responsibility to address the associated energy consumption and carbon footprint. This responsibility extends to the Worldwide LHC Computing Grid (WLCG), encompassing over 170 sites in 40 countries, supporting vital computing, disk, tape storage and network for LHC experiments. Ensuring efficient operational practices across these diverse sites is crucial beyond mere performance metrics. This paper introduces the HEP Benchmark suite, an enhanced suite designed to measure computing resource performance uniformly across all WLCG sites, using HEPScore23 as performance unit. The suite expands beyond assessing only the execution speed via HEPScore23. In fact the suite incorporates metrics such as machine load, memory usage, memory swap, and notably, power consumption. Its adaptability and user-friendly interface enable comprehensive acquisition of system-related data alongside benchmarking. Throughout 2023, this tool underwent rigorous testing across numerous WLCG sites. The focus was on studying compute job slot performance and correlating these with fabric metrics. Initial analysis unveiled the tool's efficacy in establishing a standardized model for compute resource utilization while pinpointing anomalies, often stemming from site misconfigurations. This paper aims to elucidate the tool's functionality and present the results obtained from extensive testing. By disseminating this information, the objective is to raise awareness within the community about this probing model, fostering broader adoption and encouraging responsible computing practices that prioritize both performance and environmental impact mitigation.
{"title":"HEP Benchmark Suite: Enhancing Efficiency and Sustainability in Worldwide LHC Computing Infrastructures","authors":"Natalia Szczepanek, David Britton, Alessandro Di Girolamo, Ewoud Ketele, Ivan Glushkov, Domenico Giordano, Ladislav Ondris, Emanuele Simili, Gonzalo Menendez Borge","doi":"arxiv-2408.12445","DOIUrl":"https://doi.org/arxiv-2408.12445","url":null,"abstract":"As the scientific community continues to push the boundaries of computing\u0000capabilities, there is a growing responsibility to address the associated\u0000energy consumption and carbon footprint. This responsibility extends to the\u0000Worldwide LHC Computing Grid (WLCG), encompassing over 170 sites in 40\u0000countries, supporting vital computing, disk, tape storage and network for LHC\u0000experiments. Ensuring efficient operational practices across these diverse\u0000sites is crucial beyond mere performance metrics. This paper introduces the HEP Benchmark suite, an enhanced suite designed to\u0000measure computing resource performance uniformly across all WLCG sites, using\u0000HEPScore23 as performance unit. The suite expands beyond assessing only the\u0000execution speed via HEPScore23. In fact the suite incorporates metrics such as\u0000machine load, memory usage, memory swap, and notably, power consumption. Its\u0000adaptability and user-friendly interface enable comprehensive acquisition of\u0000system-related data alongside benchmarking. Throughout 2023, this tool underwent rigorous testing across numerous WLCG\u0000sites. The focus was on studying compute job slot performance and correlating\u0000these with fabric metrics. Initial analysis unveiled the tool's efficacy in\u0000establishing a standardized model for compute resource utilization while\u0000pinpointing anomalies, often stemming from site misconfigurations. This paper aims to elucidate the tool's functionality and present the results\u0000obtained from extensive testing. By disseminating this information, the\u0000objective is to raise awareness within the community about this probing model,\u0000fostering broader adoption and encouraging responsible computing practices that\u0000prioritize both performance and environmental impact mitigation.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201431","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}
In high-energy physics, anti-neutrons ($bar{n}$) are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties provides an important probe for understanding the governing principles. However, this confronts significant challenges instrumentally with the electromagnetic calorimeter (EMC), a typical experimental sensor but recovering the information of incident $bar{n}$ insufficiently. In this study, we introduce Vision Calorimeter (ViC), a baseline method for anti-neutron reconstruction that leverages deep learning detectors to analyze the implicit relationships between EMC responses and incident $bar{n}$ characteristics. Our motivation lies in that energy distributions of $bar{n}$ samples deposited in the EMC cell arrays embody rich contextual information. Converted to 2-D images, such contextual energy distributions can be used to predict the status of $bar{n}$ ($i.e.$, incident position and momentum) through a deep learning detector along with pseudo bounding boxes and a specified training objective. Experimental results demonstrate that ViC substantially outperforms the conventional reconstruction approach, reducing the prediction error of incident position by 42.81% (from 17.31$^{circ}$ to 9.90$^{circ}$). More importantly, this study for the first time realizes the measurement of incident $bar{n}$ momentum, underscoring the potential of deep learning detectors for particle reconstruction. Code is available at https://github.com/yuhongtian17/ViC.
{"title":"Vision Calorimeter for Anti-neutron Reconstruction: A Baseline","authors":"Hongtian Yu, Yangu Li, Mingrui Wu, Letian Shen, Yue Liu, Yunxuan Song, Qixiang Ye, Xiaorui Lyu, Yajun Mao, Yangheng Zheng, Yunfan Liu","doi":"arxiv-2408.10599","DOIUrl":"https://doi.org/arxiv-2408.10599","url":null,"abstract":"In high-energy physics, anti-neutrons ($bar{n}$) are fundamental particles\u0000that frequently appear as final-state particles, and the reconstruction of\u0000their kinematic properties provides an important probe for understanding the\u0000governing principles. However, this confronts significant challenges\u0000instrumentally with the electromagnetic calorimeter (EMC), a typical\u0000experimental sensor but recovering the information of incident $bar{n}$\u0000insufficiently. In this study, we introduce Vision Calorimeter (ViC), a\u0000baseline method for anti-neutron reconstruction that leverages deep learning\u0000detectors to analyze the implicit relationships between EMC responses and\u0000incident $bar{n}$ characteristics. Our motivation lies in that energy\u0000distributions of $bar{n}$ samples deposited in the EMC cell arrays embody rich\u0000contextual information. Converted to 2-D images, such contextual energy\u0000distributions can be used to predict the status of $bar{n}$ ($i.e.$, incident\u0000position and momentum) through a deep learning detector along with pseudo\u0000bounding boxes and a specified training objective. Experimental results\u0000demonstrate that ViC substantially outperforms the conventional reconstruction\u0000approach, reducing the prediction error of incident position by 42.81% (from\u000017.31$^{circ}$ to 9.90$^{circ}$). More importantly, this study for the first\u0000time realizes the measurement of incident $bar{n}$ momentum, underscoring the\u0000potential of deep learning detectors for particle reconstruction. Code is\u0000available at https://github.com/yuhongtian17/ViC.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201433","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}
Nick Smith, Daniel Spitzbart, Jennet Dickinson, Jon Wilson, Lindsey Gray, Kelci Mohrman, Saptaparna Bhattacharya, Andrea Piccinelli, Titas Roy, Garyfallia Paspalaki, Duarte Fontes, Adam Martin, William Shepherd, Sergio Sánchez Cruz, Dorival Goncalves, Andrei Gritsan, Harrison Prosper, Tom Junk, Kyle Cranmer, Michael Peskin, Andrew Gilbert, Jonathon Langford, Frank Petriello, Luca Mantani, Andrew Wightman, Charlotte Knight, Prasanth Shyamsundar, Aashwin Basnet, Giacomo Boldrini, Kevin Lannon
The LPC EFT workshop was held April 25-26, 2024 at the University of Notre Dame. The workshop was organized into five thematic sessions: "how far beyond linear" discusses issues of truncation and validity in interpretation of results with an eye towards practicality; "reconstruction-level results" visits the question of how best to design analyses directly targeting inference of EFT parameters; "logistics of combining likelihoods" addresses the challenges of bringing a diverse array of measurements into a cohesive whole; "unfolded results" tackles the question of designing fiducial measurements for later use in EFT interpretations, and the benefits and limitations of unfolding; and "building a sample library" addresses how best to generate simulation samples for use in data analysis. This document serves as a summary of presentations, subsequent discussions, and actionable items identified over the course of the workshop.
{"title":"EFT Workshop at Notre Dame","authors":"Nick Smith, Daniel Spitzbart, Jennet Dickinson, Jon Wilson, Lindsey Gray, Kelci Mohrman, Saptaparna Bhattacharya, Andrea Piccinelli, Titas Roy, Garyfallia Paspalaki, Duarte Fontes, Adam Martin, William Shepherd, Sergio Sánchez Cruz, Dorival Goncalves, Andrei Gritsan, Harrison Prosper, Tom Junk, Kyle Cranmer, Michael Peskin, Andrew Gilbert, Jonathon Langford, Frank Petriello, Luca Mantani, Andrew Wightman, Charlotte Knight, Prasanth Shyamsundar, Aashwin Basnet, Giacomo Boldrini, Kevin Lannon","doi":"arxiv-2408.11229","DOIUrl":"https://doi.org/arxiv-2408.11229","url":null,"abstract":"The LPC EFT workshop was held April 25-26, 2024 at the University of Notre\u0000Dame. The workshop was organized into five thematic sessions: \"how far beyond\u0000linear\" discusses issues of truncation and validity in interpretation of\u0000results with an eye towards practicality; \"reconstruction-level results\" visits\u0000the question of how best to design analyses directly targeting inference of EFT\u0000parameters; \"logistics of combining likelihoods\" addresses the challenges of\u0000bringing a diverse array of measurements into a cohesive whole; \"unfolded\u0000results\" tackles the question of designing fiducial measurements for later use\u0000in EFT interpretations, and the benefits and limitations of unfolding; and\u0000\"building a sample library\" addresses how best to generate simulation samples\u0000for use in data analysis. This document serves as a summary of presentations,\u0000subsequent discussions, and actionable items identified over the course of the\u0000workshop.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201432","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}
Using 7.93 fb$^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector, we measure the absolute branching fractions of $D^0to K^-e^+nu_e$, $D^0to K^-mu^+nu_mu$, $D^+to bar K^0e^+nu_e$, and $D^+to bar K^0mu^+nu_mu$ to be $(3.509pm0.009_{rm stat.}pm0.013_{rm syst.}) %$, $(3.408pm0.011_{rm stat.}pm0.013_{rm syst.}) %$, $(8.856pm0.039_{rm stat.}pm0.078_{rm syst.}) %$, and $(8.661pm0.046_{rm stat.}pm0.080_{rm syst.}) %$, respectively. By performing a simultaneous fit to the partial decay rates of these four decays, the product of the hadronic form factor $f^K_+(0)$ and the modulus of the $cto s$ CKM matrix element $|V_{cs}|$ is determined to be $f^K_+(0)|V_{cs}|=0.7162pm0.0011_{rm stat.}pm0.0012_{rm syst.}$. Taking the value of $|V_{cs}|=0.97349pm0.00016$ from the standard model global fit or that of $f^K_+(0)=0.7452pm0.0031$ from the LQCD calculation as input, we derive the results $f^K_+(0)=0.7357pm0.0011_{rm stat.}pm0.0012_{rm syst.}$ and $|V_{cs}|=0.9611pm0.0015_{rm stat.}pm0.0016_{rm syst.}pm0.0040_{rm LQCD}$.
{"title":"Improved measurements of $D^0 to K^-ell^+ν_ell$ and $D^+ to bar K^0ell^+ν_ell$","authors":"BESIII Collaboration","doi":"arxiv-2408.09087","DOIUrl":"https://doi.org/arxiv-2408.09087","url":null,"abstract":"Using 7.93 fb$^{-1}$ of $e^+e^-$ collision data collected at the\u0000center-of-mass energy of 3.773 GeV with the BESIII detector, we measure the\u0000absolute branching fractions of $D^0to K^-e^+nu_e$, $D^0to K^-mu^+nu_mu$,\u0000$D^+to bar K^0e^+nu_e$, and $D^+to bar K^0mu^+nu_mu$ to be\u0000$(3.509pm0.009_{rm stat.}pm0.013_{rm syst.}) %$, $(3.408pm0.011_{rm\u0000stat.}pm0.013_{rm syst.}) %$, $(8.856pm0.039_{rm stat.}pm0.078_{rm\u0000syst.}) %$, and $(8.661pm0.046_{rm stat.}pm0.080_{rm syst.}) %$,\u0000respectively. By performing a simultaneous fit to the partial decay rates of\u0000these four decays, the product of the hadronic form factor $f^K_+(0)$ and the\u0000modulus of the $cto s$ CKM matrix element $|V_{cs}|$ is determined to be\u0000$f^K_+(0)|V_{cs}|=0.7162pm0.0011_{rm stat.}pm0.0012_{rm syst.}$. Taking the\u0000value of $|V_{cs}|=0.97349pm0.00016$ from the standard model global fit or\u0000that of $f^K_+(0)=0.7452pm0.0031$ from the LQCD calculation as input, we\u0000derive the results $f^K_+(0)=0.7357pm0.0011_{rm stat.}pm0.0012_{rm syst.}$\u0000and $|V_{cs}|=0.9611pm0.0015_{rm stat.}pm0.0016_{rm syst.}pm0.0040_{rm\u0000LQCD}$.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201434","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}
Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing ($10-100,{rm m})$ between them. In this letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep learning-driven super-resolution of data events. These ``improved'' events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional ``virtual'' optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies.
冰立方中微子观测站（IceCube NeutrinoObservatory）等中微子望远镜最近的发现广泛依赖于机器学习（ML）工具，以便从检测到的原始光子命中推断物理量。中微子望远镜的构建算法受限于光学模块对光子的稀疏采样，因为它们之间的间距相对较大（$10-100,{rm}）$。在这封信中，我们提出了一种新技术，通过使用深度学习驱动的数据事件超分辨率来学习探测器介质中的光子传输。这些 "改进的 "事件可以使用传统或 ML 技术重新构建，从而提高分辨率。我们的策略是在现有探测器的几何结构中布置额外的 "虚拟 "光学模块，并训练一个卷积神经网络来预测这些虚拟光学模块上的命中率。我们的研究表明，这种技术改进了一般冰基中子望远镜中μ介子的角度重建。我们的结果很容易扩展到水基中微子望远镜和其他事件形态。
{"title":"Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution","authors":"Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles","doi":"arxiv-2408.08474","DOIUrl":"https://doi.org/arxiv-2408.08474","url":null,"abstract":"Recent discoveries by neutrino telescopes, such as the IceCube Neutrino\u0000Observatory, relied extensively on machine learning (ML) tools to infer\u0000physical quantities from the raw photon hits detected. Neutrino telescope\u0000reconstruction algorithms are limited by the sparse sampling of photons by the\u0000optical modules due to the relatively large spacing ($10-100,{rm m})$ between\u0000them. In this letter, we propose a novel technique that learns photon transport\u0000through the detector medium through the use of deep learning-driven\u0000super-resolution of data events. These ``improved'' events can then be\u0000reconstructed using traditional or ML techniques, resulting in improved\u0000resolution. Our strategy arranges additional ``virtual'' optical modules within\u0000an existing detector geometry and trains a convolutional neural network to\u0000predict the hits on these virtual optical modules. We show that this technique\u0000improves the angular reconstruction of muons in a generic ice-based neutrino\u0000telescope. Our results readily extend to water-based neutrino telescopes and\u0000other event morphologies.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201438","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}
A combination of searches for the single production of vector-like top quarks ($T$) is presented. These analyses are based on proton$-$proton collisions at $sqrt{s}=13$ TeV recorded in 2015$-$2018 with the ATLAS detector at the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$^{-1}$. The $T$-quark decay modes considered in this combination are into a top quark and either a Standard Model Higgs boson or a $Z$ boson ($T to Ht$ and $T to Zt$). The individual searches used in the combination are differentiated by the number of leptons ($e$, $mu$) in the final state. The observed data are found to be in good agreement with the Standard Model background prediction. Interpretations are provided for a range of masses and couplings of the vector-like top quark for benchmark models and generalized representations in terms of 95% confidence level limits. For a benchmark signal prediction of a vector-like top quark SU2 singlet with electroweak coupling, $kappa$, of 0.5, masses below 2.1 TeV are excluded, resulting in the most restrictive limits to date.
本文介绍了对类矢量顶夸克（$T$）单次产生的组合搜索。这些分析是基于大型强子对撞机上的ATLAS探测器在2015-2018年记录的13TeV下的质子-质子对撞，对应于139 fb$^{-1}$的综合光度。在这个组合中考虑的$T$-夸克衰变模式是变成顶夸克和标准模型希格斯玻色子或$Z$玻色子（$T to Ht$和$T to Zt$）。组合中使用的各个搜索是根据最终态中轻子（$e$, $mu$）的数量来区分的。观测到的数据与标准模型的背景预言非常吻合。对于基准模型和广义表示的矢量样顶夸克的一系列质量和耦合，提供了95%置信度限值的解释。对于电弱耦合为0.5的类矢量顶夸克SU2单子的基准信号预测，排除了低于2.1 TeV的质量，从而得出了迄今为止最严格的限值。
{"title":"Combination of searches for singly produced vector-like top quarks in pp collisions at $sqrt{s} = 13$ TeV with the ATLAS detector","authors":"ATLAS Collaboration","doi":"arxiv-2408.08789","DOIUrl":"https://doi.org/arxiv-2408.08789","url":null,"abstract":"A combination of searches for the single production of vector-like top quarks\u0000($T$) is presented. These analyses are based on proton$-$proton collisions at\u0000$sqrt{s}=13$ TeV recorded in 2015$-$2018 with the ATLAS detector at the Large\u0000Hadron Collider, corresponding to an integrated luminosity of 139 fb$^{-1}$.\u0000The $T$-quark decay modes considered in this combination are into a top quark\u0000and either a Standard Model Higgs boson or a $Z$ boson ($T to Ht$ and $T to\u0000Zt$). The individual searches used in the combination are differentiated by the\u0000number of leptons ($e$, $mu$) in the final state. The observed data are found\u0000to be in good agreement with the Standard Model background prediction.\u0000Interpretations are provided for a range of masses and couplings of the\u0000vector-like top quark for benchmark models and generalized representations in\u0000terms of 95% confidence level limits. For a benchmark signal prediction of a\u0000vector-like top quark SU2 singlet with electroweak coupling, $kappa$, of 0.5,\u0000masses below 2.1 TeV are excluded, resulting in the most restrictive limits to\u0000date.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201439","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}
R. S. James, K. Rule, E. Barberio, V. U. Bashu, L. J. Bignell, I. Bolognino, G. Brooks, S. S. Chhun, F. Dastgiri, A. R. Duffy, M. Froehlich, T. M. A. Fruth, G. Fu, G. C. Hill, K. Janssens, S. Kapoor, G. J. Lane, K. T. Leaver, P. McGee, L. J. McKie, P. C. McNamara, J. McKenzie, W. J. D. Melbourne, M. Mews, L. J. Milligan, J. Mould, F. Nuti, F. Scutti, Z. Slavkovska, N. J. Spinks, O. Stanley, A. E. Stuchbery, B. Suerfu, G. N. Taylor, P. Urquijo, A. G. Williams, Y. Xing, Y. Y. Zhong, M. J. Zurowski
The persistence of the DAMA/LIBRA (DAMA) modulation over the past two decades has been a source of great contention within the dark matter community. The DAMA collaboration reports a persistent, modulating event rate within their setup of NaI(Tl) scintillating crystals at the INFN Laboratori Nazionali del Gran Sasso (LNGS) underground laboratory. A recent work alluded that this signal could have arisen due to an analysis artefact, caused by DAMA not accounting for time variation of decaying background radioisotopes in their analysis procedure. In this work, we examine in detail this 'induced modulation' effect, arguing that a number of aspects of the DAMA signal are incompatible with an induced modulation arising from decays of background isotopes over the lifetime of the experiment. Using a toy model of the DAMA/LIBRA experiment, we explore the induced modulation effect under different variations of the activities of the relevant isotopes - namely, $^3$H and $^{210}$Pb - highlighting the various inconsistencies between the resultant toy datasets and the DAMA signal. We stress the importance of the SABRE experiment, whose goal is to unambiguously test for the presence of such a modulating signal in an experiment using the same target material and comparable levels of background.
{"title":"The DAMA/LIBRA signal: an induced modulation effect?","authors":"R. S. James, K. Rule, E. Barberio, V. U. Bashu, L. J. Bignell, I. Bolognino, G. Brooks, S. S. Chhun, F. Dastgiri, A. R. Duffy, M. Froehlich, T. M. A. Fruth, G. Fu, G. C. Hill, K. Janssens, S. Kapoor, G. J. Lane, K. T. Leaver, P. McGee, L. J. McKie, P. C. McNamara, J. McKenzie, W. J. D. Melbourne, M. Mews, L. J. Milligan, J. Mould, F. Nuti, F. Scutti, Z. Slavkovska, N. J. Spinks, O. Stanley, A. E. Stuchbery, B. Suerfu, G. N. Taylor, P. Urquijo, A. G. Williams, Y. Xing, Y. Y. Zhong, M. J. Zurowski","doi":"arxiv-2408.08697","DOIUrl":"https://doi.org/arxiv-2408.08697","url":null,"abstract":"The persistence of the DAMA/LIBRA (DAMA) modulation over the past two decades\u0000has been a source of great contention within the dark matter community. The\u0000DAMA collaboration reports a persistent, modulating event rate within their\u0000setup of NaI(Tl) scintillating crystals at the INFN Laboratori Nazionali del\u0000Gran Sasso (LNGS) underground laboratory. A recent work alluded that this\u0000signal could have arisen due to an analysis artefact, caused by DAMA not\u0000accounting for time variation of decaying background radioisotopes in their\u0000analysis procedure. In this work, we examine in detail this 'induced\u0000modulation' effect, arguing that a number of aspects of the DAMA signal are\u0000incompatible with an induced modulation arising from decays of background\u0000isotopes over the lifetime of the experiment. Using a toy model of the\u0000DAMA/LIBRA experiment, we explore the induced modulation effect under different\u0000variations of the activities of the relevant isotopes - namely, $^3$H and\u0000$^{210}$Pb - highlighting the various inconsistencies between the resultant toy\u0000datasets and the DAMA signal. We stress the importance of the SABRE experiment,\u0000whose goal is to unambiguously test for the presence of such a modulating\u0000signal in an experiment using the same target material and comparable levels of\u0000background.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201437","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}
BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, W. N. Lan, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, X. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Y. H. Meng, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, Bo Wang, C. Wang, D. Y. Wang, H. J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, N. Y. Wang, S. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, X. Wu, X. H. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. P. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Y. Z. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, C. Z. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, J. Zu
Using $(10087pm44)times10^6J/psi$ events collected with the BESIII detector, we search for the rare decay $J/psi to gamma D^0+c.c.$ for the first time. No obvious signal is observed and the upper limit on the branching fraction is determined to be ${cal B}(J/psi to gamma D^{0}+c.c.)< 9.1 times 10^{-8}$ at 90% confidence level.
利用BESIII探测器收集到的$(10087/pm44)/times10^6J/psi$事件，我们首次搜索了稀有衰变$J/psi to gamma D^{0}+c.c.$。没有观察到明显的信号，在90%的置信度下，分支分数的上限被确定为${cal B}(J/psi to gamma D^{0}+c.c.)< 9.1 （10^{-8}次）$。
{"title":"Search for the rare decay $J/ψto γD^0+c.c.$ at BESIII","authors":"BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, W. N. Lan, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, X. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Y. H. Meng, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, Bo Wang, C. 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Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, C. Z. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, J. Zu","doi":"arxiv-2408.08826","DOIUrl":"https://doi.org/arxiv-2408.08826","url":null,"abstract":"Using $(10087pm44)times10^6J/psi$ events collected with the BESIII\u0000detector, we search for the rare decay $J/psi to gamma D^0+c.c.$ for the\u0000first time. No obvious signal is observed and the upper limit on the branching\u0000fraction is determined to be ${cal B}(J/psi to gamma D^{0}+c.c.)< 9.1\u0000times 10^{-8}$ at 90% confidence level.","PeriodicalId":501181,"journal":{"name":"arXiv - PHYS - High Energy Physics - Experiment","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201436","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}
New particles beyond the Standard Model of particle physics, such as axions, can be effectively searched through their interactions with electrons. We use the large liquid xenon detector PandaX-4T to search for novel electronic recoil signals induced by solar axions, neutrinos with anomalous magnetic moment, axion-like particles, dark photons, and light fermionic dark matter. A detailed background model is established with the latest datasets with 1.54 $rm tonne cdot year$ exposure. No significant excess above the background has been observed, and we have obtained competitive constraints for axion couplings, neutrino magnetic moment, and fermionic dark matter interactions.
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