The science case of the FRS Ion Catcher for FAIR Phase-0
Bibliographic record
Abstract
The FRS Ion Catcher at GSI enables precision experiments with thermalized projectile and fission fragments. At the same time it serves as a test facility for the Low-Energy Branch of the Super-FRS at FAIR. The FRS Ion Catcher has been commissioned and its performance has been characterized in five experiments with 238 U and 124 Xe projectile and fission fragments produced at energies in the range from 300 to 1000 MeV/u. High and almost element-independent efficiencies for the thermalization of short-lived nuclides produced at relativistic energies have been obtained. High-accuracy mass measurements of more than 30 projectile and fission fragments have been performed with a multiple-reflection time-of-flight mass spectrometer (MR-TOF-MS) at mass resolving powers of up to 410,000, with production cross sections down to the microbarn-level, and at rates down to a few ions per hour. The versatility of the MR-TOF-MS for isomer research has been demonstrated by the measurement of various isomers, determination of excitation energies and the production of a pure isomeric beam. Recently, several instrumental upgrades have been implemented at the FRS Ion Catcher. New experiments will be carried out during FAIR Phase-0 at GSI, including direct mass measurements of neutron-deficient nuclides below 100 Sn and neutron-rich nuclides below 208 Pb, measurement of β -delayed neutron emission probabilities and reaction studies with multi-nucleon transfer.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".