High Granularity Electromagnetic Calorimeter Shower Images

<p>Each HDF5 file contains energy deposits (shower images) created by <strong>electrons</strong> in one of the two calorimeters, for a specific incident angle of particles. Each HDF5 file has a structure of datasets, where each dataset represents&nbsp;energy deposits for a specific particle energy (in GeV). Particle energies are ranging from <strong>1</strong> to <strong>1024 GeV </strong>in powers of 2 and particle angles are ranging from <strong>50</strong> to <strong>90 degrees</strong> in a step of 10 (angle of 90 degrees indicates a particle entering the detector perpendicularly). Each dataset has the following structure<strong> {number of events,18,50,45}</strong>, with <strong>18x50x45</strong> being the granularity of a shower image.</p> <p>The calorimeter used to produce those data is a setup of concentric cylinders (layers). Each layer consists of active and passive material. The <strong>SiW</strong> geometry has 90 layers of 1.4 mm of tungsten as passive absorber and 0.3 mm of silicon as active material. The <strong>SciPb</strong> geometry has 45 layers of 4.4 mm of lead and 1.2 mm of scintillator. The number of readout cells is <strong>RxPxZ=18x50x45=40500</strong>, representing the cylindrical segmentation (rho,phi,z). The size of a single cell has been chosen to correspond to (approximately) 0.25 Moliere radius along the R axis and 0.5 radiation length along Z axis.</p> <p><br> The samples were created with the <strong><a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04</a> </strong>Geant4 example, which&nbsp; demonstrates how to use Machine Learning inference to create energy deposits as a fast simulation model using LWTNN and ONNX runtime.</p>