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High Energy Physics Calorimeter Detector Simulation Using Generative Adversarial Networks With Domain Related Constraints
Generative Adversarial Networks (GANs) have gained notoriety by generating highly realistic images. The present work explores GAN for simulating High Energy Physics detectors, interpreting detector output as three-dimensional images. The demands and requirements of a scientific simulation are quite...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1109/access.2021.3101946 http://cds.cern.ch/record/2810005 |
Sumario: | Generative Adversarial Networks (GANs) have gained notoriety by generating highly realistic
images. The present work explores GAN for simulating High Energy Physics detectors, interpreting detector
output as three-dimensional images. The demands and requirements of a scientific simulation are quite
stringent, as compared to the domain of visual images. Image characteristics such as pixel intensity and
sparsity, for example, have very different distributions. Moreover, detector simulation requires conditioning
on physics inputs, and domain knowledge becomes essential. We, therefore, adjust the pre-processing and
incorporate physics-based constraints in the loss function. We also introduce a multi-step training process
based on transfer learning by breaking up the task complexity. Validation of the results primarily consists of a
detailed comparison to full Monte Carlo in terms of several physics quantities where a high level of agreement
is found (ranging from a few percent up to 10% across a large particle energy range). In addition, we assess
the performance by physics unrelated metrics, thereby proving further the variability and pertinence through
diverse standpoints. We have demonstrated that an image generation technique from vision can successfully
simulate highly complex physics processes while achieving a speedup of more than three orders of magnitude
in comparison to the standard Monte Carlo. |
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