Tootfinder

The role of magnetic fields in ram pressure stripping of satellite galaxies in the circumgalactic medium around massive galaxies
Thomas A. Rintoul (Cardiff), Freeke van de Voort (Cardiff), Andrew T. Hannington (Cardiff), R\"udiger Pakmor (MPA), Rebekka Bieri (Zurich), Maria Werhahn (MPA), Rosie Y. Talbot (MPA)
https://arxiv…

The role of magnetic fields in ram pressure stripping of satellite galaxies in the circumgalactic medium around massive galaxies
The presence of magnetic fields in galaxies and their haloes could have important consequences for satellite galaxies moving through the magnetised circumgalactic medium (CGM) of their host. We therefore study the effect of magnetic fields on ram pressure stripping of satellites in the CGM of massive galaxies. We use cosmological `zoom-in' simulations of three massive galaxy haloes ($M_{\rm{200c}} = 10^{12.5-13}$M$_\odot$), each simulated with and without magnetic fields. The fraction of gas re…

Pressure dependence of liquid iron viscosity from machine-learning molecular dynamics
Kai Luo, Xuyang Long, R. E. Cohen
https://arxiv.org/abs/2506.21626 ht…

Pressure dependence of liquid iron viscosity from machine-learning molecular dynamics
We have developed a machine-learning potential that accurately models the behavior of iron under the conditions of Earth's core. By performing numerous nanosecond scale equilibrium molecular dynamics simulations, the viscosities of liquid iron for the whole outer core conditions are obtained with much less uncertainty. We find that the Einstein-Stokes relation is not accurate for outer core conditions. The viscosity is on the order of 10s \si{mPa.s}, in agreement with previous first-principles …

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Thomas King, Sergio Vinciguerra
https://arxiv.org/abs/2505.21312 https://

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Understanding how faults nucleate and grow is a critical problem in earthquake science and hazard assessment. This study examines fault development in Alzo granite under triaxial pressures ranging from 5 to 40 MPa by applying a Time Delay Neural Network (TDNN) to multi-parameter acoustic emission (AE) data. The TDNN integrates waveform-derived attributes, including peak delay and scattering attenuation, with occurrence-based metrics such as time distributions, Gutenberg-Richter b-values, and sp…