Population study of Galactic supernova remnants at very high<i>γ</i>-ray energies with H.E.S.S.
Bibliographic record
Abstract
Shell-type supernova remnants (SNRs) are considered prime candidates for the acceleration of Galactic cosmic rays (CRs) up to the knee of the CR spectrum atE≈ 3 × 1015eV. Our Milky Way galaxy hosts more than 350 SNRs discovered at radio wavelengths and at high energies, of which 220 fall into the H.E.S.S. Galactic Plane Survey (HGPS) region. Of those, only 50 SNRs are coincident with a H.E.S.S source and in 8 cases the very high-energy (VHE) emission is firmly identified as an SNR. The H.E.S.S. GPS provides us with a legacy for SNR population study in VHEγ-rays and we use this rich data set to extract VHE flux upper limits from all undetected SNRs. Overall, the derived flux upper limits are not in contradiction with the canonical CR paradigm. Assuming this paradigm holds true, we can constrain typical ambient density values around shell-type SNRs ton≤ 7 cm−3and electron-to-proton energy fractions above 10 TeV toϵep≤ 5 × 10−3. Furthermore, comparisons of VHE with radio luminosities in non-interacting SNRs reveal a behaviour that is in agreement with the theory of magnetic field amplification at shell-type SNRs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".