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Morphological Study of a Sample of Dwarf Tidal Galaxies Using the C-A Plane

2022· article· en· W4283080605 on OpenAlexfundno aff
I. Vega-Acevedo, A. M. Hidalgo-Gámez

Bibliographic record

VenueRevista Mexicana de Astronomía y Astrofísica · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryYork UniversityJohns Hopkins UniversityUniversity of WashingtonCarnegie Mellon UniversityCollege of Engineering, Michigan State UniversityHarvard UniversityOhio State UniversityUniversity of ArizonaJet Propulsion LaboratoryPrinceton UniversityInstituto Politécnico NacionalNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale UniversityBrookhaven National LaboratoryCalifornia Institute of TechnologyNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsGalaxyAsymmetryDwarf galaxyDwarf spheroidal galaxyAstronomyLuminous infrared galaxyGalaxy group

Abstract

fetched live from OpenAlex

In this investigation, we determined the Concentration (C) and Asymmetry (A) parameters in a sample of tidal dwarf galaxies (TDG) or candidate galaxies. Most of the galaxies in the sample were found to be in a very precise region of the C-A plane, which clearly separates them from other galaxies. In addition, the stellar mass (Mstar) and the star formation rate (SFR) in the sample were determined using optical images and GALEX observations. The main results are: the Mstar and the SFR in the TDG sample do not follow a linear correlation with the C and A respectively, as observed in the rest of galaxies, and the Mstar and the SFR have a linear correlation similar to that followed by galaxies at high redshift. Then, we can conclude that the C-A plane can be a useful method for the morphological identification of candidates for TDG or dwarf objects from very turbulent environments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.268
Teacher spread0.242 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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