Simulating Hadronic-to-Quark-Matter with Burn-UD: Recent work and astrophysical applications
Bibliographic record
Abstract
We present the new developments in Burn-UD, our in-house hydrodynamic combustion code used to model the phase transition of hadronic-to-quark matter. Our two new modules add neutrino transport and the time evolution of a (u, d, s) quark star (QS). Preliminary simulations show that the inclusion of neutrino transport points towards new hydrodynamic instabilities that increase the burning speed. A higher burning speed could elicit the deflagration to detonation of a neutron star (NS) into a QS. We propose that a Quark-Nova (QN: the explosive transition of a NS to a QS) could help us explain the most energetic astronomical events to this day: superluminous supernovae (SLSNe). Our models consider a QN occurring in a massive binary, experiencing two common envelope stages and a QN occurring after the supernova explosion of a Wolf-Rayet (WO) star. Both models have been successful in explaining the double humped light curves of over half a dozen SLSNe. We also introduce SiRop our r-process simulation code and propose that a QN site has the hot temperatures and neutron densities required to make it an ideal site for the r-process.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".