GRMHD Simulations of Neutron-star Mergers with Weak Interactions: r-process Nucleosynthesis and Electromagnetic Signatures of Dynamical Ejecta
Bibliographic record
Abstract
Abstract Fast neutron-rich material ejected dynamically over ≲10 ms during the merger of a binary neutron star (BNS) can give rise to distinctive electromagnetic counterparts to the system’s gravitational-wave emission that serve as a “smoking gun” to distinguish between a BNS and an NS–black hole merger. We present novel ab initio modeling of the kilonova precursor and kilonova afterglow based on 3D general-relativistic magnetohydrodynamic simulations of BNS mergers with nuclear, tabulated, finite-temperature equations of state (EOSs), weak interactions, and approximate neutrino transport. We analyze dynamical mass ejection from 1.35–1.35 M ⊙ binaries, consistent with properties of the first observed BNS merger GW170817, using three nuclear EOSs that span the range of allowed compactness of 1.35 M ⊙ -neutron stars. Nuclear reaction network calculations yield a robust second-to-third-peak r -process. We find few ×10 −6 M ⊙ of fast ( v > 0.6 c ) ejecta that give rise to broadband synchrotron emission on ∼years timescales, consistent with tentative evidence for excess X-ray/radio emission following GW170817. We find ≈2 × 10 −5 M ⊙ of free neutrons that power a kilonova precursor on ≲ hours timescale. A boost in early UV/optical brightness by a factor of a few due to previously neglected relativistic effects, with enhancements up to ≲10 hr post-merger, is promising for future detection with UV/optical telescopes like Swift or ULTRASAT. We find that a recently predicted opacity boost due to highly ionized lanthanides at ≳70,000 K is unlikely to affect the early kilonova based on the obtained ejecta structures. Azimuthal inhomogeneities in dynamical ejecta composition for soft EOSs found here (“lanthanide/actinide pockets”) may have observable consequences for both early kilonova and late-time nebular emission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".