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Record W4309736345 · doi:10.3847/1538-4357/ac940a

Distances, Radial Distribution, and Total Number of Galactic Supernova Remnants

2022· article· en· W4309736345 on OpenAlexaff
S. Ranasinghe, D. A. Leahy

Bibliographic record

VenueThe Astrophysical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysicsSupernovaAstrophysicsGalaxyGalaxy rotation curveRange (aeronautics)Molecular cloudMaserSelection (genetic algorithm)StarsGalaxy formation and evolution

Abstract

fetched live from OpenAlex

Abstract We present a table of 215 supernova remnants (SNRs) with distances. New distances are found to SNR G51.26 + 0.11 of 6.6 ± 1.7 kpc using H i absorption spectra and to five other SNRs using maser/molecular cloud associations. We recalculate the distances and errors to all SNRs using a consistent rotation curve and provide errors where they were not previously estimated. This results in a significant distance revisions for 20 SNRs. Because of observational constraints and selection effects, there is an apparent deficit of observed number of Galactic SNRs. To investigate this, we employ two methods. The first method applies correction factors for the selection effects to derive the radial density distribution. The second method compares functional forms for the SNR surface density and selection function against the data to find which functions are consistent with the data. The total number of SNRs in the Galaxy is ∼3500 (Method 1) or in the range from ∼2400 to ∼5600 (Method 2). We conclude that the current observed number of SNRs is not yet complete enough to give a well-determined total SNR number or radial density function.

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 score0.386
Threshold uncertainty score0.524

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations57
Published2022
Admission routes1
Has abstractyes

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