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Record W2012792114 · doi:10.1051/0004-6361:20065895

Radio spectrum and distance of the SNR HB9

2006· article· en· W2012792114 on OpenAlexafffundabout
D. A. Leahy, W. W. Tian

Bibliographic record

VenueAstronomy and Astrophysics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSpectral indexPhysicsSupernova remnantAstrophysicsGalactic planePulsarSpectral lineAstronomySupernovaGalaxy

Abstract

fetched live from OpenAlex

New images are presented of the supernova remnant (SNR) HB9 based on 408 MHz and 1420 MHz continuum emission and HI-line emission data from the Canadian Galactic Plane Survey (CGPS). Two different T-T plot methods and new integrated flux densities give spectral index ( ν) for the whole of HB9 of 0.48 ± 0.03; 0.49; and 0.47 ± 0.06, respectively. These values are lower than the previous spectral index estimated for HB9 (α = 0.61). The change is mainly due to improved 1420 MHz data. No difference in spectral index is detected between strong and weak filaments. A new result is that the spectral index for interior regions is steeper than for the rim. This can be explained by a standard curved interstellar electron energy spectrum combined with lower interior magnetic field compared to that near the outer shock. This results in a larger proportion of steep spectrum emission for lines-of-sight through the central body of the SNR. HI observations show structures probably associated with the SNR in the radial velocity range -3 to -9 km s-1, suggesting a kinematic distance of 0.8 ± 0.4 kpc for the SNR. This is consistent with the distance to the radio pulsar PSR B0458+46, offset from the center of HB9 by 23′. However the pulsar spindown and kinematic ages are significantly greater than estimates of the SNR age: the Sedov age for HB9 is 6600 yr and the evaporative cloud model yields ages of 4000–7000 yr.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.704

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)0.0000.000

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 head0.002
GPT teacher head0.163
Teacher spread0.160 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations66
Published2006
Admission routes3
Has abstractyes

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