Resonant shattering flares in black hole-neutron star and binary neutron star mergers
Bibliographic record
Abstract
ABSTRACT Resonant shattering flares (RSFs) are bursts of gamma-rays expected to be triggered by tidal resonance of a neutron star (NS) during binary inspiral. They are strongly dependent on the magnetic field strength at the surface of the NS. By modelling these flares as being the result of multiple colliding relativistic shells launched during the resonance window, we find that the prompt non-thermal gamma-ray emission may have luminosity up to a few $\times 10^{48}\rm{ erg\,s}^{-1}$, and that a broad-band afterglow could be produced. We compute the expected rates of detectable RSFs using the BPASS population synthesis code, with different assumptions about the evolution of surface magnetic field strengths before merger. We find the rate of detectable RSFs to be ∼0.0001–5 per year for BHNS mergers and ∼0.0005–25 per year for NSNS mergers, with the lower bound corresponding to surface-field decay consistent with magneto-thermal evolution in purely crustal fields, while the upper bounds are for systems that have longer lived surface magnetic fields supported by flux frozen into the superconducting core. If some of the observed SGRB precursor flares are indeed RSFs, this suggests the presence of a longer lived surface field for some fraction of the NS population, and that we could expect RSFs to be the most common detectable EM counterpart to GW detections of BHNS mergers. The non-detection of an RSF prior to GRB170817A provides an upper bound on the magnetic fields of the progenitor NSs of Bsurf ∼ 1013.5G.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".