DeepMuon: Accelerating Cosmic-Ray Muon Simulation Based on Optimal Transport

TL;DR

DeepMuon is a deep learning model that significantly accelerates cosmic muon simulation by learning muon distributions efficiently, reducing computational costs, and enabling rapid underwater muon imaging simulations.

Contribution

The paper introduces DeepMuon, a novel deep learning approach that improves the speed and accuracy of cosmic muon simulation, especially in underwater environments, using advanced statistical transformations and loss functions.

Findings

DeepMuon outperforms traditional simulation tools like CRY in speed.

It accurately learns muon distributions from limited data.

The pipeline enables rapid underwater muon radiography simulations.

Abstract

Cosmic muon imaging technology is increasingly being applied in various fields. However, simulating cosmic muons typically requires the rapid generation of a large number of muons and tracking their complex trajectories through intricate structures. This process is highly computationally demanding and consumes significant CPU time. To address these challenges, we introduce DeepMuon, an innovative deep learning model designed to efficiently and accurately generate cosmic muon distributions. In our approach, we employ the inverse Box-Cox transformation to reduce the kurtosis of the muon energy distribution, making it more statistically manageable for the model to learn. Additionally, we utilize the Sliced Wasserstein Distance (SWD) as a loss function to ensure precise simulation of the high-dimensional distributions of cosmic muons. We also demonstrate that DeepMuon can accurately learn muon distribution patterns from a limited set of data, enabling it to simulate real-world cosmic muon distributions as captured by detectors. Compared to traditional tools like CRY, DeepMuon significantly increases the speed of muon generation at sea level. Furthermore, we have developed a pipeline using DeepMuon that directly simulates muon distributions in underwater environments, dramatically accelerating simulations for underwater muon radiography and tomography. For more details on our open-source project, please visit https://github.com/wangab0/deepmuon.