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Record W2928430996

Correcting the rotation curve of spiral galaxies for the non-circular motions induced by a bar

2017· dissertation· en· W2928430996 on OpenAlexfundno aff
T. H. Randriamampandry

Bibliographic record

VenueOpen University of Cape Town (University of Cape Town) · 2017
Typedissertation
Languageen
FieldEngineering
TopicAstronomical Observations and Instrumentation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Science and Technology, Ministry of Science and Technology, IndiaNational Research Foundation
KeywordsGalaxy rotation curveSpiral (railway)Spiral galaxyRotation (mathematics)Bar (unit)AstrophysicsCircular motionPhysicsGalaxyGeometryMathematicsGalaxy formation and evolutionClassical mechanicsMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

The mass distribution of disk galaxies is usually determined through the use of rotation curves. This determination relies on two key assumptions; that the gas moves on circular orbits and that this motion traces the underlying gravitational potential. In the case of barred spiral galaxies the first assumption is false as the bar induces non-circular streaming motions in the gas. Therefore, the rotation curves of barred galaxies need to be corrected for the non-circular motions before being used for mass model analysis. In this dissertation, we use numerical simulations to quantify and correct for the non-circular flows induced by a bar. The aim is to investigate and quantify the effect of the bar properties on the amplitude of the non-circular motions. This is done by comparing the observational data such as rotation curves and bar properties with the kinematics and bar properties obtained from mocked galaxies. In chapter III, we examine the performance of ROTCUR and DISKFIT for deriving rotation curves from velocity maps of barred spiral galaxies using mock observations. Our results confirm that ROTCUR under-/overestimates measured rotation curves if the bar is aligned with one of the symmetry axes. The DISKFIT algorithm, which is specifically designed for barred galaxies only works for galaxies of intermediate bar orientations. In chapter IV, we quantify the magnitude of the non-circular flows and constraint the range of bar orientation angles where DiskFit fails by using Tree-SPH simulations from the GalMer database by Chilingarian et al . (2010 ). We found that the rotation curve obtained from ROTCUR was 40% smaller/larger than the expected velocities calculated from the gravitational potential when the bar is aligned with the major/minor axis. For the DISKFIT analysis, we found that DiskFit produces unrealistic values for all the models when the bar is within ten degrees of the symmetry axes. New hydrodynamic simulations of three disc galaxies (NGC 1300 , NGC 1530 and NGC 3621 ) are presented in chapter IV. Our objective is to create more realistic simulated galaxies that replicate the bar properties and velocity elds of the galaxies of our sample. The initial conditions for our simulations are determined through a Bayesian analysis of the azimuthally averaged rotation curve, the stellar surface brightness, and the gas surface density. The parameters posterior distribution functions (PDFs) combine with the disc stability parameters PDFs are used as criterion to select the model parameters. The velocities of the gas particles are transformed into velocity maps and compared with the observed galaxies. We are able to reproduce the bar properties and kinematics of the three galaxies in our sample. These findings imply that a tailored simulation is an effective way of investigating non-circular flows in disc galaxies especially when the bar orientation is close to the minor or major kinematical axis of the galaxy.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.971

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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 designOther design
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

Citations0
Published2017
Admission routes1
Has abstractyes

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