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The SAURON project - XII. Kinematic substructures in early-type galaxies: evidence for discs in fast rotators

2008· article· en· W2130224813 on OpenAlexaff
Davor Krajnović, Roland Bacon, Michele Cappellari, Roger L. Davies, P. T. de Zeeuw, Éric Emsellem, J. Falcón‐Barroso, H. Kuntschner, Richard M. McDermid, R. F. Peletier, M. Sarzi, Remco C. E. van den Bosch, Glenn van de Ven

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Nautical Research Society
FundersInstitut national des sciences de l'UniversCentre National de la Recherche ScientifiqueScience and Technology Facilities CouncilInstituto de Astrofísica de CanariasNederlandse Organisatie voor Wetenschappelijk OnderzoekSpace Telescope Science InstituteNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsPhotometry (optics)Stellar kinematicsKinematicsVelocity dispersionGalaxyStarsClassical mechanics

Abstract

fetched live from OpenAlex

We analysed two-dimensional maps of 48 early-type galaxies obtained with the SAURON and OASIS integral-field spectrographs using kinemetry, a generalization of surface photometry to the higher order moments of the line-of-sight velocity distribution (LOSVD). The maps analysed include: reconstructed image, mean velocity, velocity dispersion, h3 and h4 Gauss–Hermite moments. Kinemetry is a good method to recognize structures otherwise missed by using surface photometry, such as embedded discs and kinematic subcomponents. In the SAURON sample, we find that 31 per cent of early-type galaxies are single component systems. 91 per cent of the multicomponents systems have two kinematic subcomponents, the rest having three. In addition, 29 per cent of galaxies have kinematically decoupled components, nuclear components with significant kinematic twists. We differentiate between slow and fast rotators using velocity maps only and find that fast-rotating galaxies contain discs with a large range in mass fractions to the main body. Specifically, we find that the velocity maps of fast rotators closely resemble those of inclined discs, except in the transition regions between kinematic subcomponents. This deviation is measured with the kinemetric k5/k1 ratio, which is large and noisy in slow rotators and about 2 per cent in fast rotators. In terms of E/S0 classification, this means that 74 per cent of Es and 92 per cent of S0s have components with disc-like kinematics. We suggest that differences in k5/k1 values for the fast and slow rotators arise from their different intrinsic structure which is reflected on the velocity maps. For the majority of fast rotators, the kinematic axial ratios are equal to or less than their photometric axial ratios, contrary to what is predicted with isotropic Jeans models viewed at different inclinations. The position angles of fast rotators are constant, while they vary abruptly in slow rotators. Velocity dispersion maps of face-on galaxies have shapes similar to the distribution of light. Velocity dispersion maps of the edge-on fast rotators and all slow rotators show differences which can only be partially explained with isotropic models and, in the case of fast rotators, often require additional cold components. We constructed local (bin-by-bin) h3–V/σ and h4–V/σ diagrams from SAURON observations. We confirm the classical anticorrelation of h3 and V/σ, but we also find that h3 is almost zero in some objects or even weakly correlated with V/σ. The distribution of h4 for fast and slow rotators is mildly positive on average. In general, fast rotators contain flattened components characterized by a disc-like rotation. The difference between slow and fast rotators is traceable throughout all moments of the LOSVD, with evidence for different intrinsic shapes and orbital contents and, hence, likely different evolutionary paths.

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)0.002
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.237
Teacher spread0.221 · 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

Citations215
Published2008
Admission routes1
Has abstractyes

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