Electromagnetic Calorimeter Shower Images

Published: 8 May 2017| Version 1 | DOI: 10.17632/pvn3xc3wy5.1
, Michela Paganini


Each HDF5 file has the following structure: `energy Dataset {100000, 1}` `layer_0 Dataset {100000, 3, 96}` `layer_1 Dataset {100000, 12, 12}` `layer_2 Dataset {100000, 12, 6}` `overflow Dataset {100000, 3}` In practice, each file is a collection of 100,000 calorimeter showers corresponding to the particle specified in the file name (eplus = positrons, gamma = photons, piplus = charged pions). The calorimeter we built is segmented longitudinally into three layer with different depths and granularities. In units of mm, the three layers have the following (eta, phi, z) dimensions: Layer 0: (5, 160, 90) | Layer 1: (40, 40, 347) | Layer 2: (80, 40, 43) In the HDF5 files, the `energy` entry specifies the true energy of the incoming particle in units of GeV. `layer_0`, `layer_1`, and `layer_2` represents the energy deposited in each layer of the calorimeter in an image data format. Given the segmentation of each calorimeter layer, these images have dimensions 3x96 (in layer 0), 12x12 (in layer 1), and 12x6 (in layer 3). The `overflow` contains the amount of energy that was deposited outside of the calorimeter section we are considering.


Steps to reproduce

To generate datasets like this one, follow the instructions at https://github.com/hep-lbdl/CaloGAN) and use the code provided in the `generation` folder.


Yale University


Particle Physics, Machine Learning, High Energy Detector