A new model for the origin of very metal poor stars and their chemical composition
Bibliographic record
Abstract
The genesis and chemical patterns of the metal-poor stars in the galactic halo remain an open question. Current models do not seem to give a satisfactory explanation for the observed abundances of lithium in the galactic metal-poor stars and the existence of carbon-enhanced metal-poor (CEMP) and nitrogen-enhanced metal-poor (NEMP) stars. In order to deal with some of these theoretical issues, we suggest an alternative explanation, where some of the Pop III supernovae (SNe) are followed by the detonation of their neutron stars [quark-novae (QNe)]. In QNe occurring a few days to a few weeks following the preceding SN explosion, the neutron-rich relativistic QN ejecta leads to spallation of 56Ni processed in the ejecta of the preceding SN explosion and thus to ‘iron/metal impoverishment’ of the primordial gas swept by the combined SN+QN ejecta. We show that the generation of stars formed from fragmentation of pristine clouds swept up by the combined SN+QN ejecta acquires a metallicity with −7.5 < [Fe/H] < −1.5 for dual explosions with 2 < tdelay (d) < 30. Spallation leads to the depletion of 56Ni and formation of sub-Ni elements such as Ti, V, Cr and Mn providing a reasonable account of the trends observed in galactic halo metal-poor stars. CEMP stars form in dual explosions with short delays (tdelay < 5 d). These lead to important destruction of 56Ni (and thus to a drastic reduction of the amount of Fe in the swept-up cloud) while preserving the carbon processed in the outer layers of the SN ejecta. Lithium is produced from the interaction of the neutron-rich QN ejecta with the outer (oxygen-rich) layers of the SN ejecta. A lithium plateau with 2 < A(Li) < 2.4 can be produced in our model as well as a corresponding 6Li plateau with 6Li/7Li < 0.3.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".