INTERACTIONS OF COSMIC RAYS IN THE ATMOSPHERE: GROWTH CURVES REVISITED
Bibliographic record
Abstract
Measurements of cosmic-ray abundances on balloons are affected by interactions in the residual atmosphere above the balloon. Corrections for such interactions are particularly important for observations of rare secondary particles such as boron, antiprotons, and positrons. These corrections either can be calculated if the relevant cross sections in the atmosphere are known or may be empirically determined by extrapolation of the "growth curves," i.e., the individual particle intensities as functions of atmospheric depth. The growth-curve technique is particularly attractive for long-duration balloon flights where the periodic daily altitude variations permit rather precise determinations of the corresponding particle intensity variations. We determine growth curves for nuclei from boron ( Z = 5) to iron ( Z = 26) using data from the 2006 Arctic balloon flight of the TRACER detector for cosmic-ray nuclei, and we compare the growth curves with predictions from published cross section values. In general, good agreement is observed. We then study the boron/carbon abundance ratio and derive a simple and energy-independent correction term for this ratio. We emphasize that the growth-curve technique can be developed further to provide highly accurate tests of published interaction cross section values.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".