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Record W2105585915 · doi:10.1017/s1743921313005164

Stellar discs in massive galaxies

2012· article· en· W2105585915 on OpenAlexaff
D. Krajnović, Katherine Alatalo, Leo Blitz, Maxime Bois, F. Bournaud, Martin Bureau, Michele Cappellari, Roger L. Davies, T. A. Davis, P. T. de Zeeuw, Éric Emsellem, Sadegh Khochfar, H. Kuntschner, Richard M. McDermid, R. Morganti, Thorsten Naab, M. Sarzi, Nicholas Scott, P. Serra, Anne-Marie Weijmans, L. M. Young

Bibliographic record

VenueProceedings of the International Astronomical Union · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Toronto
FundersScience and Technology Facilities Council
KeywordsPhysicsAstrophysicsGalaxyAngular momentumAstronomyClassical mechanics

Abstract

fetched live from OpenAlex

Excluding those unsettled systems undergoing mergers, bright galaxies come in two flavours: with and without discs. In this work we look for photometric evidence for presence of discs and compare it with kinematic results of the ATLAS 3D survey (Cappellari et al . 2011). We fit a Sérsic (1968) function to azimuthally averaged light profiles of ATLAS 3D galaxies to derive single component fits and, subsequently, we fit a combination of the Sérsic function (free index n ) and an exponential function ( n =1) with the purpose of decomposing the light profiles into “bulge” and “disc” components (B+D model) of all non-barred sample galaxies. We compare the residuals of the B+D models with those of the single Sérsic fits and select the B+D model as preferred only when the improvement is substantial and there are no correlations within residuals. We find that the high angular momentum objects (fast rotators) are disc dominated systems with bulges of typically low n (when their light profiles can be decomposed) or are best represented with a single Sérsic function with a low Sérsic index ( n <3). Single component systems with large Sérsic indices are characteristic of low angular momentum objects (slow rotators).

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 score0.068
Threshold uncertainty score0.389

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 communication0.0000.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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