arXiv subjects

Yu-Xuan Hu

Publications and source records attributed to Yu-Xuan Hu.

3 recordsLinked to original sources

arXiv2024

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

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.

hep-ex↗

arXiv2024

SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory

High-energy physics experiments rely heavily on precise measurements of energy and momentum, yet face significant challenges due to detector limitations, calibration errors, and the intrinsic nature of particle interactions. Traditional unfolding techniques have been employed to correct for these distortions, yet they often suffer from model dependency and stability issues. We present a novel method, SwdFold, which utilizes the principles of optimal transport to provide a robust, model-independent framework to estimate the probability density ratio for data unfolding. It not only unfold the toy experimental event by reweighted simulated data distributions closely with true distributions but also maintains the integrity of physical features across various observables. We can expect it can enable more reliable predictions and comprehensive analyses as a high precision reweighting and unfolding tool in high-energy physics.

physics.data-an↗

arXiv2023

Event generation and consistency tests with sliced Wasserstein distance in high-energy physics

In the field of modern high-energy physics research, there is a growing emphasis on utilizing deep learning techniques to optimize event simulation, thereby expanding the statistical sample size for more accurate physical analysis. Traditional simulation methods often encounter challenges when dealing with complex physical processes and high-dimensional data distributions, resulting in slow performance. To overcome these limitations, we propose a solution based on deep learning with the sliced Wasserstein distance as the loss function. By employing an advanced transformer learning architecture, we initiate the learning process from a Monte Carlo sample and generate high-dimensional data. Through the integration of the sliced Wasserstein distance with the permutation test, we introduce a novel, statistically rigorous, and more sensitive metric for assessing the distribution differences, which significantly outperforms other metrics in detecting subtle distributional shifts, further validating its effectiveness for precise evaluation in high-energy physics generative models and high-dimensional consistency test. The generated data samples maintain all the original distribution features from a limited number of training samples, as evidenced by their successful passage of all common consistency tests using a test sample size of the same order of statistical magnitude. This development opens up new possibilities for improving event simulation and high-dimensional consistency tests in high-energy physics research.

physics.comp-ph↗