Experimentally well-constrained masses of 27P and 27S: Implications for studies of explosive binary systems
Bibliographic record
Abstract
The mass of 27 P is expected to impact the X-ray burst (XRB) model predictions of burst light curves and the composition of the burst ashes, but large uncertainties and inconsistencies still exist in the reported 27 P masses. We have used the β -decay spectroscopy of 27 S to determine the most precise mass excess of 27 P to date to be − 659 ( 9 ) keV, which is 63 keV (2.3 σ ) higher and a factor of 3 more precise than the value recommended in the 2016 Atomic Mass Evaluation. Based on the new 27 P mass, the Si 26 ( p , γ ) 27 P reaction rate and its uncertainty were recalculated using Monte Carlo techniques. We also estimated the previously unknown mass excess of 27 S to be 17678(77) keV, based on the measured β -delayed two-proton energy and the Coulomb displacement energy relations. The impact of these well-constrained masses and reaction rates on the modeling of the explosive astrophysical scenarios has been investigated by post-processing XRB and hydrodynamic nova models. Compared to the model calculations based on the masses and rates from databases, the abundance of A = 26 in the burst ashes is increased by a factor of 2.4, while no substantial change was found in the XRB energy generation rate or the light curve. Our calculation also suggests that 27 S is not a significant waiting point in the rapid proton capture process, and the change of the Si 26 ( p , γ ) 27 P reaction rate is not sufficiently large to affect the conclusion previously drawn on the nova contribution to the synthesis of galactic 26 Al.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".