Probing keV mass QCD axions with the SACLA X-ray free electron laser
(2026)
Modeling partially ionized dense plasma using wavepacket molecular dynamics
Physical Review E American Physical Society (APS) (2026)
Measurement of ion acceleration and diffusion in a laser-driven magnetized plasma
Nature Communications Nature Research (2026)
Abstract:
Here we present results from an experiment performed at the GSI Helmholtz Center for Heavy Ion Research. A mono-energetic beam of chromium ions with initial energies of ~ 450 MeV was fired through a magnetized interaction region formed by the collision of two counter-propagating laser-ablated plasma jets. While laser interferometry revealed the absence of strong fluid-scale turbulence, acceleration and diffusion of the beam ions was driven by wave-particle interactions. A possible mechanism is particle acceleration by electrostatic, short scale length kinetic turbulence, such as the lower-hybrid drift instability.Time-embedded convolutional neural networks for modeling plasma heat transport
Physical Review E American Physical Society (APS) 113:3 (2026) 035303
Abstract:
We introduce a time-embedded convolutional neural network (TCNN) for modeling spatiotemporal heat transport in plasmas, particularly under strongly nonlocal conditions. In our earlier work, the Luciani-Mora-Virmont (LMV) Informed Neural Network (LINN) (Luo , ) combined prior knowledge from the LMV model with kinetic Particle-in-Cell (PIC) data to improve kernel-based heat-flux predictions. While effective under moderately nonlocal conditions, LINN produced physically inconsistent kernels in strongly time-dependent regimes due to its reliance on the quasistationary LMV formulation. To overcome this limitation, TCNN is designed to capture the coupled evolution of both the normalized heat flux and the characteristic nonlocality parameter using a unified neural architecture informed by underlying physical principles. Trained on fully kinetic PIC simulations, TCNN accurately reproduces nonlocal dynamics across a broad range of collisionalities. Our results demonstrate that the combination of time modulation, coupled prediction, and convolutional depth significantly enhances predictive performance, offering a data-driven yet physically consistent framework for multiscale plasma transport problems.Diagnostic x-ray source using electrons produced by a 100 J-class picosecond laser *
Plasma Physics and Controlled Fusion IOP Publishing 68:3 (2026) 035004