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Record W2015010829 · doi:10.1093/mnras/stv094

Mapping galaxy encounters in numerical simulations: the spatial extent of induced star formation

2015· article· en· W2015010829 on OpenAlexafffund
Jorge Moreno, Paul Torrey, Sara L. Ellison, David R. Patton, Asa F. L. Bluck, Gunjan Bansal, Lars Hernquist

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsTrent UniversityUniversity of Victoria
FundersMitacsCanadian Institute for Theoretical AstrophysicsJohns Hopkins UniversityFAS Division of Science, Harvard UniversityHarvard University
KeywordsPhysicsAstrophysicsStar formationGalaxyGalaxy mergerGalaxy formation and evolutionAstronomyEccentricity (behavior)Lenticular galaxy

Abstract

fetched live from OpenAlex

We employ a suite of 75 simulations of galaxies in idealized major mergers (stellar mass ratio ∼2.5:1), with a wide range of orbital parameters, to investigate the spatial extent of interaction-induced star formation. Although the total star formation in galaxy encounters is generally elevated relative to isolated galaxies, we find that this elevation is a combination of intense enhancements within the central kpc and moderately suppressed activity at larger galactocentric radii. The radial dependence of the star formation enhancement is stronger in the less massive galaxy than in the primary, and is also more pronounced in mergers of more closely aligned disc spin orientations. Conversely, these trends are almost entirely independent of the encounter's impact parameter and orbital eccentricity. Our predictions of the radial dependence of triggered star formation, and specifically the suppression of star formation beyond kpc-scales, will be testable with the next generation of integral-field spectroscopic surveys.

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)0.000
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 score0.031
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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

Citations161
Published2015
Admission routes2
Has abstractyes

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