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Record W4399423232 · doi:10.3847/1538-3881/ad5447

Utilizing Voronoi Tessellations to Determine Radial Density Profiles

2024· preprint· en· W4399423232 on OpenAlexafffund
Veronika Dornan, William E. Harris

Bibliographic record

VenueThe Astronomical Journal · 2024
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVoronoi diagramCentroidal Voronoi tessellationGeometryGeographyGeologyMathematics

Abstract

fetched live from OpenAlex

Abstract We have developed a novel method of determining 2D radial density profiles for astronomical systems of discrete objects using Voronoi tessellations. This Voronoi-based method was tested against the standard annulus-based method on five simulated systems of objects, following known Hubble density profiles of varying parameters and sizes. It was found that the Voronoi-based method returned radial density fits with lower uncertainties on the fitting parameters across all five systems compared to the annulus-based method. The Voronoi-based method also consistently returned more accurate estimates of the total number of objects in each system than the annulus-based method, and this accuracy increased with increasing system size. Finally, the Voronoi-based method was applied to two observed globular cluster systems around brightest cluster galaxies ESO 444-G046 and 2MASX J13272961-3123237 and the results were compared to previous results for these galaxies obtained with the annulus-based method. Again, it was found that the Voronoi-based method returned fits with lower uncertainties on the fitting parameters, and the total number of globular clusters returned are within errors of the annulus-based method estimates, however also with lower uncertainties.

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 score0.343
Threshold uncertainty score0.806

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 integrity0.0000.002
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 head0.031
GPT teacher head0.264
Teacher spread0.233 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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