Preprocessed Dataset for ``Calorimetric Measurement of Multi-TeV Muons via Deep Regression" (Q6720576)

Thisrecord contains the fully-preprocessed training/validation and testing datasets used to train and evaluate the final models forCalorimetric 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 athttps://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 of886,716 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 contains429,750 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.