Abstract
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider need, so the experiment is in urgent need of new fast simulation techniques. The application of Generative Adversarial Networks is a promising solution to speed up the simulation while providing the necessary physics performance. In this paper we propose the Self-Attention Generative Adversarial Network as a possible improvement of the network architecture. The application is demonstrated on the performance of generating responses of the LHCb type of the electromagnetic calorimeter.
Highlights
The Large Hadron Collider (LHC) built by the European Organization for Nuclear Research (CERN) is the world’s largest collider
The most reasonable difference between the samples from WGAN and Self-Attention Generative Adversarial Network (SAGAN) models are the cells on the edge of the matrix and the shape of generated clusters
Sometimes there are some outliers that contain such relatively high-value energy cells in samples generated by WGAN, such samples are more smoothed-out in SAGAN case being closer to the original energy distribution according to PRD-AUC that was computed based on shower width along and across direction
Summary
The Large Hadron Collider (LHC) built by the European Organization for Nuclear Research (CERN) is the world’s largest collider. Calorimeters specialize in measuring specific parameters of passing particles, providing measurements of the energy of electrons, photons, and hadrons. This data allows the reconstruction of the initial collision, so the detector’s response simulation for a given input becomes the challenge. Using a baseline simulation software Geant4 [9] requires a tremendous number of CPU hours, being most computationally expensive part of the experimental program It was shown [1, 2] that it is possible to simulate the calorimeter response using deep learning techniques and Generative Adversarial Networks, but in order to apply GANs practically their performance should be properly evaluated using quality metrics and improved using additional techniques if it is possible. We propose adding Self-Attention module into previously published architecture to improve the quality of generating objects and compare previous results with the new ones generated by Self-Attention Generative Adversarial Network in terms of Area Under Precision-Recall Distribution Curve (PRD-AUC)
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