6 papers
DeepMuon: Accelerating Cosmic-Ray Muon Simulation Based on Optimal Transport
Ao-Bo Wang, Chu-Cheng Pan, Xiang Dong +5
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…
SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory
Chu-Cheng Pan, Xiang Dong, Yu-Chang Sun +4
High-energy physics experiments rely heavily on precise measurements of energy and momentum, yet face significant challenges due to detector limitations, calibration errors, and th…
PWACG: Partial Wave Analysis Code Generator supporting Newton-conjugate gradient method
Xiang Dong, Yu-Chang Sun, Chu-Cheng Pan +4
This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging…
Application of Deep Learning Methods Combined with Physical Background in Wide Field of View Imaging Atmospheric Cherenkov Telescopes
Ao-Yan Cheng, Hao Cai, Shi Chen +25
The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focus…
Event generation and consistency tests with sliced Wasserstein distance in high-energy physics
Chu-Cheng Pan, Xiang Dong, Yu-Chang Sun +4
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 statistic…
Fraction Constraint in Partial Wave Analysis
Xiang Dong, Chu-Cheng Pan, Yu-Chang Sun +4
To resolve the non-convex optimization problem in partial wave analysis, this paper introduces a novel approach that incorporates fraction constraints into the likelihood function.…