five

"Table 152" of "Differential $t\bar{t}$ cross-section measurements using boosted top quarks in the all-hadronic final state with 139 fb$^{-1}$ of ATLAS data"|高能物理数据集|粒子物理数据集

收藏
Mendeley Data2024-06-25 更新2024-06-29 收录
高能物理
粒子物理
下载链接:
https://www.hepdata.net/record/136731
下载链接
链接失效反馈
资源简介:
Measurements of single-, double-, and triple-differential cross-sections are presented for boosted top-quark pair-production in 13 TeV proton--proton collisions recorded by the ATLAS detector at the LHC. The top quarks are observed through their hadronic decay and reconstructed as large-radius jets with the leading jet having transverse momentum ($p_{\rm T}$) greater than 500 GeV. The observed data are unfolded to remove detector effects. The particle-level cross-section, multiplied by the $t\bar{t}\rightarrow WWb\bar{b}$ branching fraction and measured in a fiducial phase space defined by requiring the leading and second-leading jets to have $p_{\rm T}$ > 500 GeV and $p_{\rm T}$ > 350 GeV, respectively, is $331 \pm 3 \rm{(stat.)} \pm 39 \rm{(syst.)}$ fb. This is approximately 20% lower than the prediction of $398^{+48}_{-49}$ fb by POWHEG+PYTHIA8 with next-to-leading-order (NLO) accuracy but consistent within the theoretical uncertainties. Results are also presented at the parton level, where the effects of top-quark decay, parton showering, and hadronization are removed such that they can be compared with fixed-order next-to-next-to-leading-order (NNLO) calculations. The parton-level cross-section, measured in a fiducial phase space similar to that at particle level, is $1.94 \pm 0.02 \rm{(stat.)} \pm 0.25 \rm{(syst.)}$ pb. This agrees with the NNLO prediction of $1.96^{+0.02}_{-0.17}$ pb. Reasonable agreement with the differential cross-sections is found for most NLO models, while the NNLO calculations are generally in better agreement with the data. The differential cross-sections are interpreted using a Standard Model effective field-theory formalism and limits are set on Wilson coefficients of several four-fermion operators.
创建时间:
2023-06-28
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

全国 1∶200 000 数字地质图(公开版)空间数据库

As the only one of its kind, China National Digital Geological Map (Public Version at 1∶200 000 scale) Spatial Database (CNDGM-PVSD) is based on China' s former nationwide measured results of regional geological survey at 1∶200 000 scale, and is also one of the nationwide basic geosciences spatial databases jointly accomplished by multiple organizations of China. Spatially, it embraces 1 163 geological map-sheets (at scale 1: 200 000) in both formats of MapGIS and ArcGIS, covering 72% of China's whole territory with a total data volume of 90 GB. Its main sources is from 1∶200 000 regional geological survey reports, geological maps, and mineral resources maps with an original time span from mid-1950s to early 1990s. Approved by the State's related agencies, it meets all the related technical qualification requirements and standards issued by China Geological Survey in data integrity, logic consistency, location acc racy, attribution fineness, and collation precision, and is hence of excellent and reliable quality. The CNDGM-PVSD is an important component of China' s national spatial database categories, serving as a spatial digital platform for the information construction of the State's national economy, and providing informationbackbones to the national and provincial economic planning, geohazard monitoring, geological survey, mineral resources exploration as well as macro decision-making.

DataCite Commons 收录

DAT

DAT是一个统一的跨场景跨领域基准,用于开放世界无人机主动跟踪。它提供了24个视觉复杂的场景,以评估算法的跨场景和跨领域泛化能力,并具有高保真度的现实机器人动力学建模。

github 收录

CODrone

CODrone 是一个为无人机设计的全面定向目标检测数据集,它准确反映了真实世界条件。该数据集包含来自多个城市在不同光照条件下的广泛标注图像,增强了基准的逼真度。CODrone 包含超过 10,000 张高分辨率图像,捕获自五个城市的真实无人机飞行,涵盖了各种城市和工业环境,包括港口和码头。为了提高鲁棒性和泛化能力,它包括在正常光线、低光和夜间条件下相同场景的图像。我们采用了三种飞行高度和两种常用的相机角度,从而产生了六个不同的视角配置。所有图像都针对 12 个常见对象类别进行了定向边界框标注,总计超过 590,000 个标记实例。总体而言,这项工作构建了一个综合数据集和基准,用于城市无人机场景中的定向目标检测,旨在满足该领域的研究和实践应用需求。

arXiv 收录

ANC

美国国家语料库(American National Corpus,简称ANC)是一个大规模的电子美国英语语料库,包含多种类型文本及口语数据转录,旨在全面反映美国英语的多样性。其开放部分OANC约有1500万字,涵盖多种文体,且进行了自动标注。

anc.org 收录

DIV2K

DIV2K数据集分为: 列车数据: 从800高清高分辨率图像开始,我们获得相应的低分辨率图像,并为2、3和4个降尺度因子提供高分辨率和低分辨率图像 验证数据: 100高清晰度高分辨率图像用于生成低分辨率对应图像,低分辨率从挑战开始提供,并用于参与者从验证服务器获得在线反馈; 当挑战的最后阶段开始时,高分辨率图像将被释放。 测试数据: 100多样的图像用于生成低分辨率的相应图像; 参与者将在最终评估阶段开始时收到低分辨率图像,并在挑战结束并确定获胜者后宣布结果。

OpenDataLab 收录