A phase-imaging technique for precision mass measurements of neutron-rich nuclei with the Canadian Penning Trap mass spectrometer
Bibliographic record
Abstract
The nuclear mass is a fundamental property of a nucleus because it defines the binding energy which represents the sum of all internal interactions. Masses provide insight into nuclear structure effects and are influential in describing heavy element nucleosynthesis via the rapid neutron-capture (??) process. More nuclear data, including masses, on the neutron-rich side of stability is needed to improve upon the current understanding of the ?? process. The Canadian Penning Trap (CPT) mass spectrometer is located at the CARIBU facility where intense beams of neutron-rich nuclei are created. To take advantage of the unique beams available at CARIBU an upgrade to the phase-imaging ion-cyclotron-resonance (PI-ICR) mass measurement technique at the CPT has been completed, vastly improving the experimental sensitivity. Compared to the previous system this new technique is faster, more efficient, and offers improved mass resolution and higher mass precision. In this thesis the implementation of PI-ICR at the CPT is detailed alongside a thorough study of the associated systematic effects. A total of 54 neutron-rich nuclear masses are presented ranging between mass numbers of ?? = 104-168, including 18 which have been measured for the first time and 29 where a reduction in the mass uncertainty has been made. The majority of the new measurements are of rare-earth elements near ?? = 100. Trends along several isotopic chains are discussed and the impact of these measurements in explaining a possible formation mechanism of the rare-earth elemental abundance peak during the ?? process is given.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".