Bibliographic record
Abstract
ABSTRACT We propose that the delayed conversion of a neutron star (NS) into either a quark star (QS) or a hybrid star (HS), occurring approximately $\sim$105–109 d after the supernova (SN) explosion, injects ${\sim} 2 \times 10^{49}$ erg of thermal energy into the expanded SN ejecta. This energy, delivered over ${\sim} 40$ d via a quark-nova (QN) shock or spin-down (SpD) power of the HS, can reproduce the photometric and spectral features observed in SN 2023aew. In this model, the first light-curve peak corresponds to the $^{56}$Ni-powered SN from a stripped-envelope progenitor with a zero-age main sequence mass of ${\gtrsim} 15$–$16\, \mathrm{ M}_{\odot }$. The plateau between the two peaks may result from interaction with circumstellar material (CSM), or from SpD power of the NS prior to its conversion. The second peak is powered by the HS, a highly magnetized remnant formed through a quark matter phase capable of sustaining core magnetic fields up to ${\sim} 10^{18}$ G. A scenario involving two phases of SpD power – first from the NS and later from the HS – is compelling and supports the hypothesis that some magnetars may be HSs. The SpD energy of the HS powers the QN ejecta – outer NS layers – which then transfer energy to the SN ejecta, producing luminous fast blue optical transients (LFBOTs). This model offers a potential connection between superluminous SNe (SLSNe) and LFBOTs, with implications for high-energy astrophysics, r-process nucleosynthesis, and the physics of dense quark matter governed by Quantum Chromodynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".