Atomic and Subatomic Physics Research

Últimos artículos

• Seven-dimensional Trajectory Reconstruction for VAMOS++

  M. Rejmund, A. Lemasson · 1 de julio de 2026

  The VAMOS++ magnetic spectrometer is characterized by a large angular and momentum acceptance and highly non-linear ion optics properties requiring the use of software ion trajectory reconstruction methods to measure the ion magnetic rigidity and the trajectory length between the beam interaction po…

• Mitigating Diffusion Model Hallucinations with Dynamic Guidance

  Kostas Triaridis, Alexandros Graikos, Aggelina Chatziagapi, Grigorios G. Chrysos, Dimitris Samaras · 9 de junio de 2026

  Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score function, which in turn leads to interpolations between modes of the data distribution. Since semantic interpolations are often desirable and contribute …

• 3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks

  Haohan Yu, Zhanxu Hao, Bingzhi Li, Zejia Lu, Xiang Chen, Liang Li · 26 de mayo de 2026

  Accurate reconstruction of magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such as spherical harmonic expansion, often suffer from truncation errors that limit their precision. This study proposes an advanced Physics-Informed Neur…

• Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers

  Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi, Kazuyuki Demachi, Ritu Bhusal Chhatkuli · 17 de abril de 2026

  Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors during radiotherapy, as treatment-system latency entails target-location uncertainties. This work addresses frame forecasting in chest and liver cine MRI to compensate for such delays. We investigate RNNs trained with on…

• Learning unified control of internal spin squeezing in atomic qudits for magnetometry

  C. Z. Cao, J. Z. Han, M. Xiong, M. Deng, L. Wang, X. Lv, M. Xue · 31 de marzo de 2026

  Generating and preserving metrologically useful quantum states is a central challenge in quantum-enhanced atomic magnetometry. In multilevel atoms operated in the low-field regime, the nonlinear Zeeman (NLZ) effect is both a resource and a limitation. It nonlinearly redistributes internal spin fluct…

• Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions

  Marco Morik, Ali Hashemi, Klaus-Robert M\"uller, Stefan Haufe, Shinichi Nakajima · 12 de marzo de 2026

  Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains diffi…

• Future frame prediction in chest and liver cine MRI using the PCA respiratory motion model: comparing transformers and dynamically trained recurrent neural networks

  Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi, Kazuyuki Demachi, Ritu Bhusal Chhatkuli · 3 de febrero de 2026

  Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors in radiotherapy, as treatment-system latency entails target-location uncertainties. This work addresses frame forecasting in chest and liver cine MRI to compensate for such delays. We investigate RNNs trained with online…

• Compton Form Factor Extraction using Quantum Deep Neural Networks

  Brandon B. Le, Dustin Keller · 21 de enero de 2026

  We extract Compton form factors (CFFs) from deeply virtual Compton scattering measurements at the Thomas Jefferson National Accelerator Facility (JLab) using quantum-inspired deep neural networks (QDNNs). The analysis implements the twist-2 Belitsky-Kirchner-M\"uller formalism and employs a fitting …