Measurement of the iron spectrum in cosmic rays by VERITAS
Bibliographic record
Abstract
We present a new measurement of the energy spectrum of iron nuclei in cosmic rays from 20 TeV to 500 TeV. The measurement makes use of a template-based analysis method, which, for the first time, is applied to the energy reconstruction of iron-induced air showers recorded by the VERITAS array of imaging atmospheric Cherenkov telescopes. The event selection makes use of the direct Cherenkov light which is emitted by charged particles before the first interaction, as well as other parameters related to the shape of the recorded air shower images. The measured spectrum is well described by a power law $\frac{\mathrm{d}F}{\mathrm{d}E}={f}_{0}\ifmmode\cdot\else\textperiodcentered\fi{}{(\frac{E}{{E}_{0}})}^{\ensuremath{-}\ensuremath{\gamma}}$ over the full energy range, with $\ensuremath{\gamma}=2.82\ifmmode\pm\else\textpm\fi{}0.30{(\text{stat})}_{\ensuremath{-}0.27}^{+0.24}(\text{syst})$ and ${f}_{0}=(4.82\ifmmode\pm\else\textpm\fi{}0.98{(\text{stat})}_{\ensuremath{-}2.70}^{+2.12}(\text{syst}))\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}\text{ }\text{ }{\mathrm{m}}^{\ensuremath{-}2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{TeV}}^{\ensuremath{-}1}\text{ }{\mathrm{sr}}^{\ensuremath{-}1}$ at ${E}_{0}=50\text{ }\text{ }\mathrm{TeV}$, with no indication of a cutoff or spectral break. The measured differential flux is compatible with previous results, with improved statistical uncertainty at the highest energies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".