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Record W2056285996 · doi:10.1088/0004-637x/727/2/107

SUBLUMINOUS TYPE Ia SUPERNOVAE AT HIGH REDSHIFT FROM THE SUPERNOVA LEGACY SURVEY

2011· article· en· W2056285996 on OpenAlexaff
S. González–Gaitán, K. Perrett, M. Sullivan, A. Conley, D. A. Howell, R. G. Carlberg, P. Astier, D. Balam, C. Balland, S. Basa, D. Fouchez, J. Guy, D. Hardin, I. Hook, R. Pain, C. J. Pritchet, N. Regnault, J. Rich, C. Lidman

Bibliographic record

VenueThe Astrophysical Journal · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsUniversity of VictoriaDominion Astrophysical ObservatoryDefence Research and Development CanadaHerzberg Institute of AstrophysicsUniversity of Toronto
FundersScience and Technology Facilities Council
KeywordsAstrophysicsPhysicsSupernovaRedshiftGalaxyAstronomyPopulation

Abstract

fetched live from OpenAlex

The rate evolution of subluminous Type Ia supernovae (SNe Ia) is presented using data from the Supernova Legacy Survey.This sub-sample represents the faint and rapidly declining light curves of the observed SN Ia population here defined by low-stretch values (s 0.8).Up to redshift z = 0.6, we find 18 photometrically identified subluminous SNe Ia, of which six have spectroscopic redshift (and three are spectroscopically confirmed SNe Ia).The evolution of the subluminous volumetric rate is constant or slightly decreasing with redshift, in contrast to the increasing SN Ia rate found for the normal stretch population, although a rising behavior is not conclusively ruled out.The subluminous sample is mainly found in early-type galaxies with little or no star formation, so that the rate evolution is consistent with a galactic mass-dependent behavior: r(z) = A × M g , with A = (1.1 ± 0.3) × 10 -14 SNe yr -1 M -1 .

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)0.000
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.001

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 head0.030
GPT teacher head0.238
Teacher spread0.208 · 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

Citations53
Published2011
Admission routes1
Has abstractyes

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