7 papers
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang +1
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral c…
Reconstruction of fast-rotating neutron star observables with the neural network
Wen Liu, Lingxiao Wang, Zhenyu Zhu
Rotation can significantly affect neutron-star (NS) properties, but accurate modeling of rapidly rotating NSs requires solving a two-dimensional, axially symmetric system, making t…
Solving Functional Renormalization Group Equations with Neural Networks
Yang-yang Tan, Wei-jie Fu, Lianyi He +1
We employ deep neural networks to represent the field derivative of the scale-dependent effective potential in the functional renormalization group (fRG) framework for nonperturbat…
Learning Quantum Operator Dynamics from Short-Time Data
Jinyang Li, Satoshi Iso, Shunji Matsuura +2
Real-time dynamics of quantum observables provide direct access to excitation spectra and correlation functions in quantum many-body systems, but currently available quantum device…
Learning Hadron Emitting Sources with Deep Neural Networks
Lingxiao Wang, Jiaxing Zhao
The correlation function observed in high-energy collision experiments encodes critical information about the emitted source and hadronic interactions. While the proton-proton inte…
Deep learning for exploring hadron-hadron interactions
Lingxiao Wang
In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experimen…