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Record W4385240113 · doi:10.3847/1538-4365/acdd71

Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies

2023· article· en· W4385240113 on OpenAlexaff
Dennis Zaritsky, Richard L. Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersDivision of Astronomical SciencesJet Propulsion LaboratoryOffice of ScienceCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationHigh Energy PhysicsU.S. Department of EnergyNational Science Foundation
KeywordsGalaxyPhysicsAstrophysicsSkyStellar massStarsStellar populationSample (material)PopulationStar formationMedicine

Abstract

fetched live from OpenAlex

Abstract We present the completed catalog of ultradiffuse galaxy (UDG) candidates (7070 objects) from our search of the DR9 Legacy Survey images, including distance and total mass estimates for 1529 and 1436 galaxies, respectively, that we provide and describe in detail. From the sample with estimated distances, we obtain a sample of 585 UDGs ( μ 0, g ≥ 24 mag arcsec −2 and r e ≥ 1.5 kpc) over 20,000 square degrees of sky in various environments. We conclude that UDGs in our sample are limited to 10 10 ≲ M h / M ⊙ ≲ 10 11.5 and are on average a factor of 1.5–7 deficient in stars relative to the general population of galaxies of the same total mass. That factor increases with increasing galaxy size and mass up to a factor of ∼10 when the total mass of the UDG increases beyond M h = 10 11 M ⊙ . We do not find evidence that this factor has a dependence on the UDGs large-scale environment.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations64
Published2023
Admission routes1
Has abstractyes

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