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Record W2045782407 · doi:10.1088/0067-0049/210/1/3

A CATALOG OF BULGE, DISK, AND TOTAL STELLAR MASS ESTIMATES FOR THE SLOAN DIGITAL SKY SURVEY

2013· article· en· W2045782407 on OpenAlexaff
J. Trevor Mendel, Luc Simard, Michael J. D. Palmer, Sara L. Ellison, David R. Patton

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsNational Research Council CanadaTrent UniversityUniversity of Victoria
Fundersnot available
KeywordsBulgeAstrophysicsPhysicsPhotometry (optics)SkyStellar massSurface brightnessGalaxyAstronomyStar formationStars

Abstract

fetched live from OpenAlex

We present a catalog of bulge, disk, and total stellar mass estimates for ~660,000 galaxies in the Legacy area of the Sloan Digital Sky Survey Data Release 7. These masses are based on a homogeneous catalog of g- and r-band photometry described by Simard et al. (2011), which we extend here with bulge+disk and Sersic profile photometric decompositions in the SDSS u, i, and z bands. We discuss the methodology used to derive stellar masses from these data via fitting to broadband spectral energy distributions (SEDs), and show that the typical statistical uncertainty on total, bulge, and disk stellar mass is ~0.15 dex. Despite relatively small formal uncertainties, we argue that SED modeling assumptions, including the choice of synthesis model, extinction law, initial mass function, and details of stellar evolution likely contribute an additional 60% systematic uncertainty in any mass estimate based on broadband SED fitting. We discuss several approaches for identifying genuine bulge+disk systems based on both their statistical likelihood and an analysis of their one-dimensional surface-brightness profiles, and include these metrics in the catalogs. Estimates of the total, bulge and disk stellar masses for both normal and dust-free models and their uncertainties are made publicly available here.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.450

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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 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

Citations252
Published2013
Admission routes1
Has abstractyes

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