Cosmic ray origin: Supernova remnants through the electromagnetic spectrum
Bibliographic record
Abstract
Supernova Remnants have long been considered as unique candidates for cosmic-ray sources. Recent observations of several SNRs in X-rays and TeV gamma-rays will help in solving the problem of the origin of cosmic rays and are key to understanding the mechanism of particle acceleration at a propagating shock wave. The observation results of Galactic shell-type supernova remnants at different evolution stages Cas A, Tycho's SNR, γCygni SNR, IC 443 and G166.0+4.3 by the SHALON mirror Cherenkov telescope are presented. For each SNR the SHALON observation results are given with its spectral energy distribution compared with other experimental data and images by SHALON together with data from X-ray by Chandra and radio-data by Canadian Galactic Plane Survey DRAO (CGPS). The comparison of the source's morphology in different energy bands could reveal its essential features as a forward and reverse shock or the location of swept out dense molecular cloud. The experimental data presented here have confirmed the prediction of the theory about the hadronic generation mechanism of very high energy 800 GeV-100 TeV gamma-rays in Tycho's SNR, Cas A and IC 443. Also the collected experimental data help to make clear the origin of TeV gamma-ray emission in the SNRs like γCygni SNR and G166.0+4.3.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".