Figure data: modeling partially-ionized dense plasma using wavepacket molecular dynamics
University of Oxford (2026)
Abstract:
Figure data relating to the main text of "Modeling partially-ionized dense plasma using wavepacket molecular dynamics". All data is in the format of .txt files.Suppression of pair beam instabilities in a laboratory analogue of blazar pair cascades
Proceedings of the National Academy of Sciences National Academy of Sciences 122:45 (2025) e2513365122
Abstract:
The generation of dense electron-positron pair beams in the laboratory can enable direct tests of theoretical models of γ-ray bursts and active galactic nuclei. We have successfully achieved this using ultrarelativistic protons accelerated by the Super Proton Synchrotron at (CERN). In the first application of this experimental platform, the stability of the pair beam is studied as it propagates through a meter-length plasma, analogous to TeV γ-ray-induced pair cascades in the intergalactic medium. It has been argued that pair beam instabilities disrupt the cascade, thus accounting for the observed lack of reprocessed GeV emission from TeV blazars. If true, this would remove the need for a moderate strength intergalactic magnetic field to explain the observations. We find that the pair beam instability is suppressed if the beam is not perfectly collimated or monochromatic, hence the lower limit to the intergalactic magnetic field inferred from γ-ray observations of blazars is robust.Modeling partially-ionized dense plasma using wavepacket molecular dynamics
(2025)
Learning heat transport kernels using a nonlocal heat transport theory-informed neural network
Physical Review Research American Physical Society (APS) 7:4 (2025) L042017
Abstract:
<jats:p>We present a data-driven framework for the modeling of nonlocal heat transport in plasmas using a nonlocal theory-informed neural network trained on kinetic particle-in-cell simulations that span both local and nonlocal regimes. The model learns spatio-temporal heat flux kernels directly from simulation data, capturing dynamic transport behaviors beyond the reach of classical formulations. Unlike time-independent kernel models such as Luciani-Mora-Virmont and Schurtz-Nicolaï-Busquet models, our approach yields physically grounded, time-evolving kernels that adapt to varying plasma conditions. The resulting predictions show strong agreement with kinetic benchmarks across regimes. This offers a promising direction for data-driven modeling of nonlocal heat transport and contributes to a deeper understanding of plasma dynamics.</jats:p>Time-Embedded Convolutional Neural Networks for Modeling Plasma Heat Transport
(2025)