Omkar2118/datasets huggingface dataset:
Electromagnetic Calorimeter Shower Images

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.

Introducing paper: CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks