R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge
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This is the first R&D dataset for the LHC Olympics 2020 Anomaly Detection Challenge. It consists of 1M QCD dijet events and 100k W'->XY events, with X->qq and Y->qq. The W', X, and Y masses are 3.5 TeV, 500 GeV and 100 GeV respectively. The events are produced using Pythia8 and Delphes 3.4.1, with no pileup or MPI included. They are selected using a single fat-jet (R=1) trigger with pT threshold of 1.2 TeV. The events are randomly shuffled together, but for the purposes of testing and development, we provide the user with a signal/background truth bit for each event. Obviously, the truth bit will not be included in the actual challenge. These events are stored as pandas dataframes saved to compressed h5 format. For each event, all Delphes reconstructed particles in the event are assumed to be massless and are recorded in detector coordinates (pT, eta, phi). More detailed information such as particle charge is not included. Events are zero padded to constant size arrays of 700 particles, with the truth bit appended at the end. The array format is therefore (Nevents=1.1M, 2101). For more information, including an example Jupyter notebook illustrating how to read and process the events, see the official LHC Olympics 2020 webpage. https://lhco2020.github.io/homepage/ <strong>UPDATE May 18 2020</strong> We have uploaded a second signal dataset for R&D, consisting of 100k W'->XY with X,Y->qqq (i.e. 3-prong substructure). Everything else about this signal dataset (particle masses, trigger, Pythia configuration, detector simulation) is the same as the previous one described above. <strong>UPDATE November 23 2020</strong> We now include high-level feature files for the background and 2-prong signal (events_anomalydetection_v2.features.h5) and for the 3-prong signal (events_anomalydetection_Z_XY_qqq.features.h5). To produce the features, we have clustered every event into R=1 jets using the anti-kT algorithm. The features (calculated using fastjet plugins) are the 3-momenta, invariant masses, and n-jettiness variables tau1, tau2 and tau3 for the highest pT jet (j1) and the second highest pT jet (j2): 'pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2' The rows (events) in each feature file should be ordered exactly the same as in their corresponding raw event file. For convenience, we have also included the label (1 for signal and 0 for background) as an additional column in the first feature file (events_anomalydetection_v2.features.h5). <strong>UPDATE February 11 2021</strong> We have included the Delphes detector card and the Pythia8 command files used to produce the R&D datasets.
本数据集为LHC Olympics 2020异常检测挑战赛（LHC Olympics 2020 Anomaly Detection Challenge）的首个研发用数据集。它包含100万个量子色动力学（Quantum Chromodynamics, QCD）双喷注事件与10万个W'→XY衰变事件，其中X衰变为夸克-反夸克对（X→qq）、Y衰变为夸克-反夸克对（Y→qq）。W'、X、Y的质量分别为3.5太电子伏特（TeV）、500吉电子伏特（GeV）与100 GeV。该事件样本通过Pythia8与Delphes 3.4.1模拟生成，未包含束流叠加（pileup）与多重部分子相互作用（multiple parton interactions, MPI）。事件选取采用单一大半径喷注（R=1）触发方案，横向动量（transverse momentum, pT）阈值设为1.2 TeV。所有事件已被随机混洗；为便于测试与研发，我们为使用者提供了每个事件的信号/背景真值标签位。需注意，实际挑战赛中将不提供该真值标签位。事件以pandas数据框格式存储为压缩h5文件。对于每个事件，所有经Delphes重建的粒子均被视为无质量粒子，并以探测器坐标系下的横向动量（pT）、赝快度（pseudorapidity, eta）与方位角（azimuthal angle, phi）进行记录，未包含粒子电荷等额外详细信息。所有事件被补零至固定长度的700个粒子数组，末尾附加真值标签位，因此数组格式为（事件数Nevents=110万，2101）。如需更多信息，包括演示如何读取与处理事件的Jupyter笔记本示例，请访问LHC Olympics 2020官方网页：https://lhco2020.github.io/homepage/2020年5月18日更新：我们上传了第二份研发用信号数据集，包含10万个W'→XY衰变事件，其中X、Y均衰变为三夸克末态（X,Y→qqq，即具有三喷注子结构）。该信号数据集的其余参数（粒子质量、触发条件、Pythia配置、探测器模拟方案）与前述数据集完全一致。2020年11月23日更新：我们新增了背景与双喷注信号样本的高级特征文件（events_anomalydetection_v2.features.h5）以及三喷注信号样本的高级特征文件（events_anomalydetection_Z_XY_qqq.features.h5）。为生成这些特征，我们利用anti-kT算法将每个事件聚类为R=1的喷注。特征通过fastjet插件计算得到，包含最高横向动量喷注（j1）与次高横向动量喷注（j2）的三维动量、不变质量以及n-喷注重量变量tau1、tau2与tau3，具体特征名为：'pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2'。每个特征文件中的事件行顺序与对应原始事件文件完全一致。为便于使用，我们在第一份特征文件（events_anomalydetection_v2.features.h5）中额外附加了标签列（信号样本为1，背景样本为0）。2021年2月11日更新：我们新增了用于生成该研发数据集的Delphes探测器配置卡与Pythia8命令文件。
