Further explorations of Skyrme-Hartree-Fock-Bogoliubov mass formulas. II. Role of the effective mass
Bibliographic record
Abstract
We have constructed four new complete mass tables, referred to as (Hartree-Fock-Bogoliubov) HFB-4 to HFB-7, each one including all the 9200 nuclei lying between the two drip lines over the range of $Z$ and $N\ensuremath{\geqslant}8$ and $Z\ensuremath{\leqslant}120$. HFB-4 and HFB-5 have the isoscalar effective mass ${M}_{s}^{*}$ constrained to the value $0.92M$, with the former having a density-independent pairing, and the latter a density-dependent pairing. HFB-6 and HFB-7 are similar, except that ${M}_{s}^{*}$ is constrained to $0.8M$. The rms errors of the mass-data fits are 0.680, 0.675, 0.686, and $0.676\phantom{\rule{0.3em}{0ex}}\text{MeV}$, respectively, almost as good as for the HFB-2 mass formula, for which ${M}_{s}^{*}$ was unconstrained. However, as usual, the single-particle spectra depend significantly on ${M}_{s}^{*}$. This decoupling of the mass fits from the fits to the single-particle spectra has been achieved only by making the cutoff parameter of the $\ensuremath{\delta}$-function pairing force a free parameter. An improved treatment of the center-of-mass correction was adopted, but although this makes a difference to individual nuclei it does not reduce the overall rms error of the fit. The extrapolations of all four new mass formulas out to the drip lines are essentially the same as for the original HFB-2 mass formula.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".