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Record W7071828285

THE TYPE Ia SUPERNOVA RATE IN RADIO AND INFRARED GALAXIES FROM THE CANADA-FRANCE-HAWAII TELESCOPE SUPERNOVA LEGACY SURVEY

2010· article· en· W7071828285 on OpenAlexaboutno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsSupernovaInfraredGalaxyStar formationLuminous infrared galaxyRadio galaxyTelescopeInfrared telescope
DOInot available

Abstract

fetched live from OpenAlex

We have combined the large SNIa database of the Canada-France-Hawaii Telescope Supernova Legacy Survey and catalogs of galaxies with photometric redshifts, Very Large Array 1.4GHz radio sources, and Spitzer infrared sources. We present eight SNeIa in early-type host galaxies which have counterparts in the radio and infrared source catalogs. We find the SN Ia rate in subsets of radio and infrared early-type galaxies is ∼ 1-5 times the rate in all early-type galaxies, and that any enhancement is always ≲2σ. Rates in these subsets are consistent with predictions of the two-component "A+B" SN Ia rate model. Since infrared properties of radio SNIa hosts indicate dust-obscured star formation, we incorporate infrared star formation rates into the "A+B" model. We also show the properties of SNeIa in radio and infrared galaxies suggest the hosts contain dust and support a continuum of delay time distributions (DTDs) for SNeIa, although other DTDs cannot be ruled out based on our data. © 2010. The American Astronomical Society. All rights reserved.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.587
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.013
GPT teacher head0.240
Teacher spread0.228 · 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 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Research Archive (ORA) (University of Oxford)Same topicMachine Learning in BioinformaticsFrench-language works237,207