End-to-end event reconstruction for precision physics at future colliders dataset ML format

Dataset used for "End-to-end event reconstruction for precision physics at future colliders" Derived from HitPF_datageneration, prepared in a machine-learning friendly parquet format, ready to be used with HitPF Contents* Z--> (qq) q=uds, dataset used for training candidate clustering, we provide 200k events as a sample, the rest are hosted at CERN's EOS (paths in dataset.txt)* Particle gun dataset used for training property regression, we provide 10k events as a sample, the rest are hosted at CERN's EOS (paths in dataset.txt)* Evaluation dataset used for the results section, we provide the full 100k sample used in the paper. Each .tar file contains the dataset in parquet format with 100 events. Each dataset consists of events that can be iterated over using the pytorch dataloader provided in XX. Each event has the following information available: X_track: the input features of tracks in the event X_hit: the input features of hits in the event X_gen: the target set of particles y_gen_track: target label for track hits y_gen_hit: target label for calo hits The validation dataset additionally also has this information to compare to the baseline approach: X_pandora: pandora set of reconstructed particles pfo_calohit: label of each calo hit in the pandora set pfo_track: label of each track in the pandora set The features availanle in each are described in HitPF_datagenerationThe dataset is split in chunks of 2000 files with 100 events per file.本数据集服务于论文《面向未来对撞机精准物理的端到端事件重建》（"End-to-end event reconstruction for precision physics at future colliders"）。 本数据集源自HitPF_datageneration（HitPF），采用适配机器学习工作流的Parquet格式（Parquet）存储，可直接与HitPF框架配合使用。 ### 数据集内容 1. Z玻色子衰变至夸克对（Z→(qq)，其中夸克q为u、d、s夸克）数据集：用于候选簇团训练任务。我们提供20万个事件作为示例样本，其余数据托管于欧洲核子研究中心（CERN）的EOS存储系统，完整路径请参见dataset.txt文件。 2. 粒子枪数据集：用于属性回归训练任务。我们提供1万个事件作为示例样本，其余数据托管于CERN EOS存储系统，完整路径请参见dataset.txt文件。 3. 评估数据集：对应论文结果章节，我们提供了论文中使用的全部10万个事件样本。 每个.tar压缩包内均包含Parquet格式的数据集，单包内含100个事件。 所有数据集均可通过XX中提供的PyTorch数据加载器（PyTorch Dataloader）实现事件迭代读取，每个事件包含以下信息： - X_track：事件内径迹的输入特征 - X_hit：事件内击中点（Hit）的输入特征 - X_gen：目标粒子集合 - y_gen_track：径迹击中点的目标标签 - y_gen_hit：量能器击中点的目标标签 验证数据集额外包含以下信息，用于与基线方法进行对比： - X_pandora：Pandora算法（Pandora）重建的粒子集合 - pfo_calohit：Pandora集合中每个量能器击中点的标签 - pfo_track：Pandora集合中每个径迹的标签 各特征的详细说明可参见HitPF_datageneration文档。本数据集按块划分，每块包含2000个数据文件，每个文件内含100个事件。