Moments in time: Numerical analysis of a method for time-resolved neutron spectroscopy

Physics of Plasmas AIP Publishing 33:10 (2026) 102701

Authors:

Cb Stuart, B Appelbe, Aj Crilly, C Forrest, A DeVault, M Gatu Johnson, Bj Lahmann, D Schlossberg, Sp Regan, G Gregori

Abstract:

<jats:p>Time-resolved neutron information is essential for understanding the dynamics of inertial confinement fusion (ICF) implosions, providing key indicators that distinguish igniting from non-igniting plasmas and revealing the underlying causes of shot-to-shot performance variations. The moments method framework offers a practical pathway to extract such temporally resolved quantities using only a small number of neutron time-of-flight detectors. For example, one only needs four detectors to extract the burn-averaged rate of change of ion temperature, a well-defined physically useful quantity. In this work, we evaluate and validate the moments method across several levels of complexity. We first employ a controlled toy model to test the foundational assumptions and mathematical structure of the approach. We then demonstrate that the method generalizes to realistic one-dimensional simulation data of direct drive implosions with a 25 kJ laser driver that include time-dependent ion temperature evolution and hydrodynamic burn dynamics. Together, these results establish the moments method as an experimentally accessible and robust tool for extracting unique and independent information such as the burn-averaged rates of change of fluid velocity and ion temperature with time from neutron time-of-flight measurements, broadening the diagnostic capability of existing neutron detector systems. Fielding this diagnostic on ICF campaigns would improve our understanding of failure modes, helping to determine causes for improved or reduced performance during implosions, informing future target and laser drive design.</jats:p>

Hall-MHD in driven turbulence FLASH simulations

ArXiv 2609.03183 (2026)

Authors:

A Mohapatra, EC Hansen, A Reyes, AFA Bott, EG Blackman, P Tzeferacos

Generative Diffusion Surrogates with Analytical Variance Schedule

ArXiv 2609.01705 (2026)

Authors:

Patrick Reichherzer, Gianluca Gregori, David N Hosking, Subir Sarkar

Latent Thermal Instability

The Astrophysical Journal Letters American Astronomical Society 1008:1 (2026) l20

Authors:

Prakriti P Choudhury, Archie FA Bott

Abstract:

Multiscale temperature fluctuations are abundant in the intracluster medium (ICM) outside of galaxy cluster cores (∼100 kpc). Their origin is often attributed to turbulent stirring by subhalos or accreting baryons crossing the virial radius. However, their apparent resistance to mixing and thermal conduction in a collisional medium has not been explained. We propose a new mechanism by which steady-state temperature fluctuations can form and persist outside the cluster core. Local thermal instability, or Field instability, is used to explain filamentary condensates in cluster cores but is usually dismissed outside them because thermal conduction should suppress instability. In weakly collisional or collisionless plasmas, however, thermal conduction can be anomalously suppressed by heat-flux-driven plasma instabilities triggered in the presence of a local magnetic field, leading to two effects: (i) condensates form in a new parameter regime that overlaps with conditions outside the core, and (ii) condensates reach a steady state as in the hydrodynamic limit. This extends the regime of instability-driven fluctuations to over ≳50% (depending on hot plasma temperature) of the cluster. We use one-dimensional hydrodynamic simulations of condensates to test our analytical ideas.

Resolution-Robust Machine Learning Heat Flux Closure for Inertial Confinement Fusion Plasmas

American Physical Society (APS) 1:1 (2026) 013017

Authors:

M Luo, AR Bell, F Miniati, SM Vinko, G Gregori

Abstract:

Accurate modeling of heat flux in inertial confinement fusion plasmas requires closures that remain predictive far from local equilibrium and across disparate spatial and temporal resolutions. We develop a resolution-robust machine learning heat flux closure trained on particle-in-cell simulations using a Fourier neural operator. Two nonlocal electron thermal conduction models are trained and tested. When embedded self-consistently into the electron energy equation, the learned closure faithfully reproduces the temperature evolution and shows good temporal extrapolation and generalization capability. Remarkably, models trained on coarse-resolution data accurately predict heat flux when deployed in substantially finer-resolution implicit, iterative solvers of the energy equation, significantly enhancing the practicality of embedding data-driven closures into partial differential equation solvers. These results establish a data-driven closure that bridges kinetic and fluid descriptions and provides a viable pathway for treating machine learning as an iterative solver within the radiation-hydrodynamic simulations of inertial confinement fusion plasma.