Time-Variant Vector Field Visualization for Magnetic Fields of Neutron Star Simulations

TL;DR

Neutron stars host magnetic fields up to $10^{16}$ Gauss, and their configuration influences jets and heavy-element production in binary mergers. This paper addresses the challenge of visualizing time-variant magnetic fields in such simulations by introducing a Unity-based visualization tool that fuses dense LIC on a cross-section with sparse 3D streamlines to render time-evolving fields in real time. The method relies on preprocessing large HDF4 datasets into streaming 3D textures, a cross-section LIC, RKF45-based streamline integration, and GPU post-processing to render tubes/arrows, with an interactive time control. A qualitative usability study with domain experts reports very high usability scores and positive feedback, indicating the tool's practicality for daily astrophysical analysis. The work advances real-time, large-scale visualization for NS magnetohydrodynamics and enables more intuitive exploration of magnetic-field evolution during NS mergers, potentially aiding jet and ejecta studies.

Abstract

We present a novel visualization application designed to explore the time-dependent development of magnetic fields of neutron stars. The strongest magnetic fields in the universe can be found within neutron stars, potentially playing a role in initiating astrophysical jets and facilitating the outflow of neutron-rich matter, ultimately resulting in the production of heavy elements during binary neutron star mergers. Since such effects may be dependent on the strength and configuration of the magnetic field, the formation and parameters of such fields are part of current research in astrophysics. Magnetic fields are investigated using simulations in which various initial configurations are tested. However, the long-term configuration is an open question, and current simulations do not achieve a stable magnetic field. Neutron star simulations produce data quantities in the range of several terabytes, which are both spatially in 3D and temporally resolved. Our tool enables physicists to interactively explore the generated data. We first convert the data in a pre-processing step and then we combine sparse vector field visualization using streamlines with dense vector field visualization using line integral convolution. We provide several methods to interact with the data responsively. This allows the user to intuitively investigate data-specific issues. Furthermore, diverse visualization techniques facilitate individual exploration of the data and enable real-time processing of specific domain tasks, like the investigation of the time-dependent evolution of the magnetic field. In a qualitative study, domain experts tested the tool, and the usability was queried. Experts rated the tool very positively and recommended it for their daily work.

Figures (9)

• Figure 1: Configuration of magnetic fields in neutron stars. (a) poloidal, (b) toroidal, (c) poloidal and toroidal mixed
• Figure 2: Screenshot from the tool. The magnetic field is visualized with streamline arrows and the blue sphere indicates the neutron star.
• Figure 3: The same seed and tool configuration for twelve different time steps (t) 0, 3, 20, 50, 100, 200, 300, 400, 700, 1000, 1400, 1797 (last time step in the data set). The initial magnetic field is set as a toroidal field by default. In the first few time steps, the magnetic field initially spreads out and a lot of chaotic turbulence forms. After around 700 time steps, the field comes to rest and changes only slightly.
• Figure 4: The two handles control the rotation and position of the Cross Section. a) The rotation handle. b) The translation handle. The Cross Section image is re-calculated in real time.
• Figure 5: The pipeline to generate and render streamlines. In the On load data step, the data is loaded from the disk to RAM. In the On parameter update step, the streamlines are calculated in parallel on the CPU using the vector field dataset. Then, the curves are smoothed and attributes are computed in a compute shader. In the On frame update step, lines are cut into segments inside a compute shader, and then 3D tubes or arrows are constructed from the streamline skeletons in a geometry shader. In a fragment shader, attributes are color coded and the tubes are shaded to increase 3D perception.