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Record W4229072515 · doi:10.3847/1538-4365/ac6ceb

Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). III. The Southern SMUDGes Catalog

2022· article· en· W4229072515 on OpenAlexafffund
Dennis Zaritsky, Richard L. Donnerstein, Ananthan Karunakaran, C. E. Barbosa, Arjun Dey, Jennifer Kadowaki, Kristine Spekkens, Huanian Zhang

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaSmithsonian Astrophysical ObservatoryJet Propulsion LaboratoryNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyNational Science FoundationJohn Simon Guggenheim Memorial FoundationSmithsonian Institution
KeywordsRedshiftGalaxyExtrapolationPhysicsAstrophysicsSurface brightnessRADIUSSkyApparent SizeUpper and lower boundsEffective radiusSequence (biology)Point (geometry)StatisticsComputer scienceGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract We present a catalog of 5598 ultra-diffuse galaxy (UDG) candidates with effective radius r e > 5.″3 distributed throughout the southern portion of the DESI Legacy Imaging Survey covering ∼15,000 deg 2 . The catalog is most complete for physically large ( r e > 2.5 kpc) UDGs lying in the redshift range 1800 ≲ cz /km s −1 ≲ 7000, where the lower bound is defined by where incompleteness becomes significant for large objects on the sky and the upper bound by our minimum angular size selection criterion. Because physical size is integral to the definition of a UDG, we develop a method of distance estimation using existing redshift surveys. With three different galaxy samples, two of which contain UDGs with spectroscopic redshifts, we estimate that the method has a redshift accuracy of ∼75% when the method converges, although larger, more representative spectroscopic UDG samples are needed in order to fully understand the behavior of the method. We are able to estimate distances for 1079 of our UDG candidates (19%). Finally, to illustrate some uses of the catalog, we present both distance-independent and distance-dependent results. In the latter category, we establish that the red sequence of UDGs lies on the extrapolation of the red sequence relation for bright ellipticals and that the environment–color relation is at least qualitatively similar to that of high surface brightness galaxies. Both of these results challenge some of the models proposed for UDG evolution.

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, Insufficient 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 score0.273
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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

Citations46
Published2022
Admission routes2
Has abstractyes

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