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Record W2944322412 · doi:10.1051/epjconf/201920804006

Cosmic ray origin: Supernova remnants through the electromagnetic spectrum

2019· article· en· W2944322412 on OpenAlexaboutno aff
V. G. Sinitsyna, S.S. Borisov, R. M. Mirzafatikhov, V.Y. Sinitsyna

Bibliographic record

VenueEPJ Web of Conferences · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsCosmic rayAstrophysicsSupernovaCherenkov radiationGamma rayGalactic planeSupernova remnantParticle accelerationAstronomyTelescopeAccelerationGalaxyDetectorOptics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.012
GPT teacher head0.231
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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