Section on Supernova Remnants and Cosmic Rays of the White Paper on the Status and Future of Ground-Based Gamma-Ray Astronomy
Bibliographic record
Abstract
This is a report on the findings of the SNR/cosmic-ray working group for the white paper on the status and future of ground-based gamma-ray astronomy. The white paper is an APS commissioned document, and the overall version has also been released and can be found on astro-ph. This detailed section of the white paper discusses the status of past and current attempts to observe shell-type supernova remnants and diffuse emission from cosmic rays at GeV-TeV energies. We concentrate on the potential of future ground-based gamma-ray experiments to study the acceleration of relativistic charged particles which is one of the main unsolved, yet fundamental, problems in modern astrophysics. The acceleration of particles relies on interactions between energetic particles and magnetic turbulence. In the case of SNRs we can perform spatially resolved studies in systems with known geometry, and the plasma physics deduced from these observations will help us to understand other systems where rapid particle acceleration is believed to occur and where observations as detailed as those of SNRs are not possible.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.022 |
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".