Preprocessed Dataset for ``Calorimetric Measurement of Multi-TeV Muons via Deep Regression"

This record contains the fully-preprocessed training/validation and testing datasets used to train and evaluate the final models for "Calorimetric Measurement of Multi-TeV Muons via Deep Regression" by Jan Kieseler, Giles C. Strong, Filippo Chiandotto, Tommaso Dorigo, & Lukas Layer, (2021), arXiv:2107.02119 [physics.ins-det] (https://arxiv.org/abs/2107.02119). The files are LZF-compressed HDF5 format and designed to be used directly with the code-base available at https://github.com/GilesStrong/calo_muon_regression. Please use the 'issues' tab on the GitHub repo for any questions or problems with these datasets. The training dataset consists of 886,716 muons with energies in the continuous range [50,8000] GeV split into 36 subsamples (folds). The zeroth fold of this dataset is used as our validation data. The testing dataset contains 429,750 muons, generated at fixed values of muon energy (E=100, 500, 900, 1300, 1700, 2100, 2500, 2900, 3300, 3700, 4100 GeV), and split into 18 folds. The input features are the raw hits in the calorimeter (stored in a sparse COO representation), and the high-level features discussed in the paper.本数据集包含用于训练和评估Jan Kieseler、Giles C. Strong、Filippo Chiandotto、Tommaso Dorigo与Lukas Layer于2021年发表的论文《通过深度回归实现多TeV缪子的量热测量》（Calorimetric Measurement of Multi-TeV Muons via Deep Regression）的最终模型所用的全预处理训练、验证与测试数据集。该论文发表于2021年，收录于arXiv预印本平台编号为arXiv:2107.02119 [physics.ins-det]的条目，链接为https://arxiv.org/abs/2107.02119。该数据集文件采用LZF压缩的HDF5格式，可直接与https://github.com/GilesStrong/calo_muon_regression仓库中的代码库配合使用。若对该数据集存在任何疑问或问题，请通过GitHub仓库的Issues标签页提交。训练数据集包含886,716个缪子，其能量处于50至8000 GeV的连续区间，被划分为36个子样本（折），其中第0折作为验证数据集使用。测试数据集包含429,750个缪子，这些缪子的能量均为固定取值（E=100、500、900、1300、1700、2100、2500、2900、3300、3700、4100 GeV），并被划分为18个折。输入特征为量热仪中的原始击中记录（以稀疏COO（Coordinate List Format）表示存储），以及论文中讨论的高级特征。