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The ATLAS3D project - V. The CO Tully-Fisher relation of early-type galaxies

2011· article· en· W2153665512 on OpenAlexaff

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersScience and Technology Facilities Council
KeywordsSpiral galaxyGalaxyKinematicsInterferometryMillimeterOffset (computer science)Measure (data warehouse)COSMIC cancer database

Abstract

fetched live from OpenAlex

We demonstrate here using both single-dish and interferometric observations that CO molecules are an excellent kinematic tracer, even in high-mass galaxies, allowing us to investigate for the first time the CO Tully–Fisher relation (CO-TFR) of early-type galaxies. We compare the TFRs produced using both single-dish and interferometric data and various inclination estimation methods, and evaluate the use of the velocity profile shape as a criterion for selecting galaxies in which the molecular gas extends beyond the peak of the rotation curve. We show that the gradient and zero-point of the best-fitting relations are robust, independent of the velocity measure and inclination used, and agree with those of relations derived using stellar kinematics. We also show that the early-type CO-TFR is offset from the CO-TFR of spirals by 0.98 ± 0.22 mag at Ks band, in line with other results. The intrinsic scatter of the relation is found to be ≈0.4 mag, similar to the level found in the spiral galaxy population. Next-generation facilities such as the Large Millimeter Telescope and the Atacama Large Millimeter/Sub-millimeter Array should allow this technique to be used in higher redshift systems, providing a simple new tool to trace the mass-to-light ratio evolution of the most massive galaxies over cosmic time.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0330.030

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 head0.207
Teacher spread0.194 · 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

Citations70
Published2011
Admission routes1
Has abstractyes

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