A Black Hole Is Born: 3D General-relativistic Magnetohydrodynamic Simulation of Black Hole Formation from Core Collapse
Bibliographic record
Abstract
Abstract We present the first three-dimensional, fully general-relativistic magnetohydrodynamic (3D GRMHD) simulation of a black hole (BH) formed from the collapsed core of a massive star. The ability to self-consistently capture the birth of a compact remnant in 3D is crucial for modeling natal BH properties (including masses, spins, and kicks), which are of particular interest in the era of gravitational-wave astronomy. However, such simulations have remained elusive due to extreme computational challenges and demands. We employ the GPU-accelerated dynamical-spacetime GRMHD code GRaM-X to follow the collapse, core bounce, shock propagation, and eventual BH formation of a massive stellar progenitor in full 3D. We initialize our simulation by mapping a one-dimensional (1D) model of a star with a zero-age-main-sequence mass of 45M ⊙ to 3D. We use the core rotation velocity expected from stellar evolution modeling and a relatively weak dipolar magnetic field. The collapsing core drives a shock that reaches a maximum radius of roughly 170 km before stalling and does not lead to a successful explosion. The protoneutron star accretes matter before collapsing to form a BH t BH ≈ 325 ms after core bounce. The time of BH formation and initial BH mass are remarkably similar to those obtained with GR1D, a 1D general-relativistic neutrino-hydrodynamics code, to which we compare our results. We track the horizon of the newborn BH after formation and calculate a steady kick velocity of v kick ≈ 72 km s − 1 and a mass of M BH ≈ 2.62M ⊙, which is still rising at the end of the simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".