NEURAL - a tracking detector for neutron-induced reactions of astrophysical importance
Bibliographic record
Abstract
Observations from gamma ray telescopes indicate that most of the galactic 26 Al originates in massive stars. Several sites have been discussed for its production, including proton burning in the winds of very massive stars, and the later, explosive burning stages of these stars. Observations from the RHESSI and INTEGRAL missions currently seem to point to the latter scenario. In the advanced burning stages of massive stars the presence of neutrons becomes an important factor in nuclear reaction networks, so in addition to the 26 Al(p, γ ) 27 Si reaction the neutron capture reactions 26 Al(n,p) 26 Mg and 26 Al(n, α ) 23 Na can lead to the destruction of 26 Al, and thus alter the observed 26 Al abundance. NEURAL is a detector design to measure the excitation functions of these reactions over a wide range of energies. 26 Al targets implanted at TRIUMF will be exposed to a pulsed neutron beam at the neutron time-of-flight facility at LANSCE (Los Alamos Neutron Science CEnter). NEURAL is designed to detect all charged reaction products, combining a time projection chamber for the heavy ions, and Si detectors for the light particles mounted around the target. A first prototype has been built and partially tested at TRIUMF and LANSCE in December 2009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".