General-relativistic magnetar magnetospheres in 3D with physics-informed neural networks

TL;DR

This paper introduces a novel physics-informed neural network approach to model three-dimensional general-relativistic magnetar magnetospheres, revealing limitations on energy release and implications for observed burst phenomena.

Contribution

It develops a 3D modeling framework for magnetar magnetospheres incorporating general relativity and realistic surface currents, advancing beyond previous axisymmetric studies.

Findings

Lowest-energy solutions allow only ~30% excess energy in global twists.

3D models with localized spots show only ~5% energy excess.

Accounting for redshift effects complicates explaining magnetar burst energetics.

Abstract

Magnetar phenomena are likely intertwined with the location and structure of magnetospheric currents. General-relativistic effects are important in shaping the force-free equilibria describing static configurations, though most studies have quantified their impact only in cases of axial symmetry. Using a novel methodology based on physics-informed neural networks, fully three-dimensional configurations of varying stellar compactness are constructed. Realistic profiles for surface currents, qualitatively capturing the geometry of observed hotspots, are applied as boundary conditions to deduce the amount of free energy available to fuel outburst activity. It is found that the lowest-energy solution branches permit only a $\approx 30\%$ excess relative to current-starved solutions in axisymmetric cases with global twists, regardless of compactness, reducing to $\approx 5\%$ in 3D models with localised spots. Accounting for redshift reductions to their inferred dipole moments from timing data, explaining magnetar burst energetics therefore becomes more difficult unless the field hosts non-negligible multipoles. Discussions on other aspects of magnetar phenomena are also provided.