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Record W4391272162 · doi:10.48550/arxiv.2401.13826

The Spatial Distribution of Type Ia Supernovae within Host Galaxies

2024· preprint· en· W4391272162 on OpenAlexfundno aff
C. J. Pritchet, Karun Thanjavur, Connor Bottrell, Yan Gao

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsnot available
FundersLos Alamos National LaboratoryU.S. Naval ObservatoryFermilabMax-Planck-Institut für AstronomieNanjing UniversityMax-Planck-GesellschaftChinese Academy of SciencesNew Mexico State UniversityUniversity of PortsmouthUniversität BaselUniversity of PittsburghJohns Hopkins UniversityOhio State UniversityU.S. Department of EnergyPrinceton UniversityNational Science FoundationUniversity of WashingtonNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan FoundationDrexel UniversityNational Aeronautics and Space AdministrationCase Western Reserve University
KeywordsSupernovaBulgePhysicsAstrophysicsGalaxySkyAstronomy

Abstract

fetched live from OpenAlex

We study how type Ia supernovae (SNe Ia) are spatially distributed within their host galaxies, using data taken from the Sloan Digital Sky Survey (SDSS). This paper specifically tests the hypothesis that the SNe Ia rate traces the r-band light of the morphological component to which supernovae belong. A sample of supernovae is taken from the SDSS SN Survey, and host galaxies are identified. Each host galaxy is decomposed into a bulge and disk, and the distribution of supernovae is compared to the distribution of disk and bulge light. Our methodology is relatively unaffected by seeing. We find that in disk light dominated galaxies, SNe Ia trace light closely. The situation is less clear for bulges and ellipticals because of resolution effects, but the available evidence is also consistent with the hypothesis that bulge/elliptical SNe Ia follow light.

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)0.003
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.029
GPT teacher head0.192
Teacher spread0.163 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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