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Record W2962891736 · doi:10.3847/1538-4357/ab0e6d

A Multimass Velocity Dispersion Model of 47 Tucanae Indicates No Evidence for an Intermediate-mass Black Hole

2019· article· en· W2962891736 on OpenAlexaff
Christopher R. Mann, Harvey B. Richer, Jeremy Heyl, Jay Anderson, Jason S. Kalirai, Ilaria Caiazzo, Swantje D. Möhle, A. M. Knee, Holger Baumgardt

Bibliographic record

VenueThe Astrophysical Journal · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsGlobular clusterAstrophysicsVelocity dispersionBlack hole (networking)Mass segregationStarsIntermediate-mass black holeStellar massStar clusterStellar collisionBlue stragglerStellar dynamicsPhotometry (optics)Binary black holeStellar black holeAstronomyStellar evolutionStar formationGalaxy

Abstract

fetched live from OpenAlex

Abstract In this paper, we analyze stellar proper motions in the core of the globular cluster 47 Tucanae to explore the possibility of an intermediate-mass black hole (IMBH) influence on the stellar dynamics. Our use of short-wavelength photometry affords us an exceedingly clear view of stellar motions into the very center of the crowded core, yielding proper motions for >50,000 stars in the central 2′. We model the velocity dispersion profile of the cluster using an isotropic Jeans model. The density distribution is taken as a central IMBH point mass added to a combination of King templates. We individually model the general low-mass cluster objects (main sequence/giant stars), as well as the concentrated populations of heavy binary systems and dark stellar remnants. Using unbinned likelihood model fitting, we find that the inclusion of the concentrated populations in our model plays a crucial role in fitting for an IMBH mass. The concentrated binaries and stellar-mass black holes (BHs) produce a sufficient velocity dispersion signal in the core so as to make an IMBH unnecessary to fit the observations. We additionally determine that a stellar-mass BH retention fraction of ≳8.5% becomes incompatible with our observed velocities in the core.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.581

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 science0.0010.000
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 head0.035
GPT teacher head0.281
Teacher spread0.246 · 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 designSimulation or modeling
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

Citations172
Published2019
Admission routes1
Has abstractyes

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