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Record W1998152665 · doi:10.1088/0004-6256/135/4/1343

TYPE Ia SUPERNOVAE RATES AND GALAXY CLUSTERING FROM THE CFHT SUPERNOVA LEGACY SURVEY

2008· article· en· W1998152665 on OpenAlexaffabout
M. L. Graham, C. J. Pritchet, M. Sullivan, Stephen Gwyn, James D. Neill, E. Y. Hsiao, P. Astier, D. Balam, C. Balland, S. Basa, R. G. Carlberg, A. Conley, D. Fouchez, J. Guy, D. Hardin, I. Hook, D. A. Howell, R. Pain, K. Perrett, N. Regnault, S. Baumont, Junlong Du, C. Lidman, S. Perlmutter, P. Ripoche, N. Suzuki, E. S. Walker, T. Zhang

Bibliographic record

VenueThe Astronomical Journal · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsHerzberg Institute of AstrophysicsUniversity of TorontoUniversity of Victoria
Fundersnot available
KeywordsAstrophysicsPhysicsSupernovaGalaxyRedshiftCluster (spacecraft)Galaxy clusterAstronomyStar formation

Abstract

fetched live from OpenAlex

The Canada-France-Hawaii Telescope Supernova Legacy Survey (CFHT SNLS) has created a large homogeneous database of intermediate redshift (0.2 < z < 1.0) type Ia supernovae (SNe Ia). The SNLS team has shown that correlations exist between SN Ia rates, properties, and host galaxy star-formation rates (SFRs). The SNLS SN Ia database has now been combined with a photometric redshift galaxy catalog and an optical galaxy cluster catalog to investigate the possible influence of galaxy clustering on the SN Ia rate, over and above the expected effect due to the dependence of SFR on clustering through the morphology-density relation. We identify three cluster SNe Ia, plus three additional possible cluster SNe Ia, and find the SN Ia rate per unit mass in clusters at intermediate redshifts is consistent with the rate per unit mass in field early-type galaxies and the SN Ia cluster rate from low-redshift cluster targeted surveys. We also find the number of SNe Ia in cluster environments to be within a factor of 2 of expectations from the two-component SN Ia rate model.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.257
Teacher spread0.226 · 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.

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

Citations42
Published2008
Admission routes2
Has abstractyes

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