The 3D pulsar magnetosphere with machine learning: first results
Ioannis Dimitropoulos, Evaggelos Chaniadakis, Ioannis Contopoulos
TL;DR
This work tackles the lack of a reference steady-state solution for the 3D ideal force-free pulsar magnetosphere by developing a domain-decomposed, current-sheet-aware solver implemented with meshless Physics-Informed Neural Networks (PINNs) in the corotating frame. By solving two independent open/closed region problems and iteratively shaping the interim separatrix to satisfy pressure balance and $B^2-E^2$ continuity, the authors obtain a dissipationless steady-state solution for an inclined dipole with a fixed polar-cap shape. The results reveal a Y-point inside the light cylinder, a $B_p\to0$ condition at the Y-point with $B_\phi\neq0$, and a topologically T-shaped Y-point, along with an undulating equatorial current sheet and automatic $\alpha$-adjustment along open field lines crossing the light cylinder. The total Poynting flux is nearly conserved in the open region, yielding $L \approx 1.2L_0(1+\sin^2\lambda)$ for the test case, demonstrating the potential of PINNs to generate new ideal 3D pulsar magnetosphere solutions. While the approach ignores microphysical reconnection and current-sheet dissipation, it provides a controllable framework to explore magnetospheric topology across parameter space and offers a path toward more comprehensive models that can be linked to observations and other astrophysical systems.
Abstract
All numerical solutions of the pulsar magnetosphere over the past 25 years show closed-line regions that end a significant distance inside the light cylinder, and manifest thick strongly dissipative separatrix surfaces instead of thin current sheets, with a tip that has a distinct pointed Y shape instead of a T shape. We need to understand the origin of these results which were not predicted by our early theories of the pulsar magnetosphere. In order to gain new intuition on this problem, we set out to obtain the theoretical steady-state solution of the 3D ideal force-free magnetosphere with zero dissipation along the separatrix and equatorial current sheets. In order to achieve our goal, we needed to develop a novel numerical method. We solve two independent magnetospheric problems without current sheet discontinuities in the domains of open and closed field lines, and adjust the shape of their interface (the separatrix) to satisfy pressure balance between the two regions. The solution is obtained with meshless Physics Informed Neural Networks (PINNs). In this paper we present our first results for an inclined dipole rotator using the new methodology. We are able to zoom-in around the Y-point and inside the closed-line region, and we observe new interesting features. This is the first time the steady-state 3D problem is addressed directly, and not through a time-dependent simulation that eventually relaxes to a steady-state. We have trained a Neural Network that instantaneously yields the three components of the magnetic field and their spatial derivatives at any given point. Our results demonstrate the potential of the new method to generate new solutions of the ideal pulsar magnetosphere.
