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Galaxy pairs in the Sloan Digital Sky Survey - V. Tracing changes in star formation rate and metallicity out to separations of 80 kpc

2012· article· en· W2949884473 on OpenAlexaff
Jillian M. Scudder, Sara L. Ellison, Paul Torrey, David R. Patton, J. Trevor Mendel

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsTrent UniversityUniversity of Victoria
Fundersnot available
KeywordsPhysicsAstrophysicsMetallicityGalaxyStellar massStar formationSkyRedshiftAstronomyGalaxy formation and evolution

Abstract

fetched live from OpenAlex

We present a sample of 1899 galaxies with a close companion taken from the Sloan Digital Sky Survey Data Release 7. The galaxy pairs are selected to have velocity differences ∆v< 300 km s−1, projected separations (rp) < 80 h−170 kpc, mass ratios between 0.1 and 10, and robust measurements of star formation rates and gas-phase metallicities. We match the galaxies in total stellar mass, redshift, and local density to a set of 10 control galaxies per pair galaxy. For each pair galaxy we can therefore calculate the statistical change in star formation rate (SFR) and metallicity associated with the interaction process. Relative to the control sample, we find that galaxies in pairs show typical SFR enhancements that are, on average, 60 % higher than the control sample at rp < 30 h−170 kpc. It is at these small separations that the strongest enhancements in SFR (by up to a factor ∼10) are measured, although such starbursts are rare, even amongst the closest pairs. In addition, the pairs demonstrate more modest SFR enhancements of ∼30 % out to at least 80 h−170 kpc (the widest separations in our sample). This is the first time that enhanced SFRs have been robustly detected out to such large projected separations. Galaxies in both major and minor mergers show significant SFR enhancements

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.425

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.224
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 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

Citations253
Published2012
Admission routes1
Has abstractyes

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