“Super-kilonovae” from Massive Collapsars as Signatures of Black Hole Birth in the Pair-instability Mass Gap
Bibliographic record
Abstract
Abstract The core collapse of rapidly rotating massive ∼ 10 M ⊙ stars (“collapsars”), and the resulting formation of hyperaccreting black holes, comprise a leading model for the central engines of long-duration gamma-ray bursts (GRBs) and promising sources of r -process nucleosynthesis. Here, we explore the signatures of collapsars from progenitors with helium cores ≳ 130 M ⊙ above the pair-instability mass gap. While the rapid collapse to a black hole likely precludes prompt explosions in these systems, we demonstrate that disk outflows can generate a large quantity (up to ≳ 50 M ⊙ ) of ejecta, comprised of ≳ 5–10 M ⊙ in r -process elements and ∼ 0.1–1 M ⊙ of 56 Ni, expanding at velocities ∼0.1 c. Radioactive heating of the disk wind ejecta powers an optical/IR transient, with a characteristic luminosity ∼ 10 42 erg s −1 and a spectral peak in the near-IR (due to the high optical/UV opacities of lanthanide elements), similar to kilonovae from neutron star mergers, but with longer durations ≳1 month. These “super-kilonovae” (superKNe) herald the birth of massive black holes ≳ 60 M ⊙ , which—as a result of disk wind mass loss—can populate the pair-instability mass gap “from above,” and could potentially create the binary components of GW190521. SuperKNe could be discovered via wide-field surveys, such as those planned with the Roman Space Telescope, or via late-time IR follow-up observations of extremely energetic GRBs. Multiband gravitational waves of ∼ 0.1–50 Hz from nonaxisymmetric instabilities in self-gravitating massive collapsar disks are potentially detectable by proposed observatories out to hundreds of Mpc; in contrast to the “chirp” from binary mergers, the collapsar gravitational-wave signal decreases in frequency as the disk radius grows (“sad trombone”).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".