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Record W3197951196 · doi:10.3390/universe7090338

Search for and Identification of Young Compact Galactic Supernova Remnants Using THOR

2021· article· en· W3197951196 on OpenAlexafffund
S. Ranasinghe, D. A. Leahy, J. M. Stil

Bibliographic record

VenueUniverse · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsSupernovaAstrophysicsGalaxySupernova remnantAstronomySpectral lineAngular diameterFlux (metallurgy)Stars

Abstract

fetched live from OpenAlex

Young Supernova remnants (SNRs) with smaller angular sizes are likely missing from existing radio SNR catalogues, caused by observational constraints and selection effects. In order to find new compact radio SNR candidates, we searched the high angular resolution (25″) THOR radio survey of the first quadrant of the galaxy. We selected sources with non-thermal radio spectra. HI absorption spectra and channel maps were used to identify which sources are galactic and to estimate their distances. Two new compact SNRs were found: G31.299-0.493 and G18.760-0.072, of which the latter was a previously suggested SNR candidate. The distances to these SNRs are 5.0±0.3 kpc and 4.7±0.2 kpc, respectively. Based on the SN rate in the galaxy or on the statistics of known SNRs, we estimate that there are 15–20 not-yet detected compact SNRs in the galaxy and that the THOR survey area should contain three or four. Our detection of two SNRs (half the expected number) is consistent with the THOR sensitivity limit compared with the distribution of integrated flux densities of SNRs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations10
Published2021
Admission routes2
Has abstractyes

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