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Record W2036384042 · doi:10.1093/mnras/stt1562

Galaxy pairs in the Sloan Digital Sky Survey – VIII. The observational properties of post-merger galaxies

2013· article· en· W2036384042 on OpenAlexaff
Sara L. Ellison, J. Trevor Mendel, David R. Patton, Jillian M. Scudder

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsTrent UniversityUniversity of Victoria
Fundersnot available
KeywordsPhysicsAstrophysicsGalaxySkyGalaxy mergerControl sampleActive galactic nucleusAstronomyAccretion (finance)Star formationMetallicityStellar mass

Abstract

fetched live from OpenAlex

In order to investigate the effects of galaxy mergers throughout the interaction sequence, we present a study of 10 800 galaxies in close pairs and a smaller sample of 97 post-mergers identified in the Sloan Digital Sky Survey. We find that the average central star formation rate (SFR) enhancement (×3.5) and the fraction of starbursts (20 per cent) peak in the post-merger sample. The post-mergers also show a stronger deficit in gas phase metallicity than the closest pairs, being more metal-poor than their control by −0.09 dex. Combined with the observed trends in SFR and the time-scales predicted in merger simulations, we estimate that the post-mergers in our sample have undergone coalescence within the last few hundred Myr. In contrast with the incidence of star-forming galaxies, the frequency of active galactic nuclei (AGN) peaks in the post-mergers, outnumbering AGN in the control sample by a factor of 3.75. Moreover, amongst the galaxies that host an AGN, the black hole accretion rates in the closest pairs and post-mergers are higher by a factor of ∼3 than AGN in the control sample. These results are consistent with a picture in which star formation is initiated early on in the encounter, with AGN activity peaking post-coalescence.

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 score0.008
Threshold uncertainty score0.760

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.191
Teacher spread0.175 · 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

Citations282
Published2013
Admission routes1
Has abstractyes

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