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Record W4210742585 · doi:10.1051/0004-6361/202142627

Stellar masses, sizes, and radial profiles for 465 nearby early-type galaxies: An extension to the<i>Spitzer</i>survey of stellar structure in Galaxies (S<sup>4</sup>G)

2022· article· en· W4210742585 on OpenAlexaff
A. E. Watkins, H. Salo, E. Laurikainen, S. Díaz-García, S. Comerón, Joachim Janz, A. H. Su, R. Buta, E. Athanassoula, A. Bosma, Luis C. Ho, Benne W. Holwerda, T. Kim, J. H. Knapen, Seppo Laine, Karín Menéndez‐Delmestre, R. F. Peletier, Kartik Sheth, Dennis Zaritsky

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsQueen's University
FundersAgencia Estatal de InvestigaciónMinisterio de Ciencia e InnovaciónInstituto SerrapilheiraNational Research Foundation of KoreaJet Propulsion LaboratoryCentre National d’Etudes SpatialesFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e TecnológicoNational Natural Science Foundation of ChinaScience and Technology Facilities CouncilNational Research FoundationEuropean CommissionCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPhysicsAstrophysicsGalaxyPhotometry (optics)Surface brightnessLuminous infrared galaxyAstronomyStellar massStar formationStars

Abstract

fetched live from OpenAlex

Context. The Spitzer Survey of Stellar Structure in Galaxies (S 4 G) is a detailed study of over 2300 nearby galaxies in the near-infrared (NIR), which has been critical to our understanding of the detailed structures of nearby galaxies. Because the sample galaxies were selected only using radio-derived velocities, however, the survey favored late-type disk galaxies over lenticulars and ellipticals. Aims. A follow-up Spitzer survey was conducted to rectify this bias, adding 465 early-type galaxies (ETGs) to the original sample, to be analyzed in a manner consistent with the initial survey. We present the data release of this ETG extension, up to the third data processing pipeline (P3): surface photometry. Methods. We produce curves of growth and radial surface brightness profiles (with and without inclination corrections) using reduced and masked Spitzer IRAC 3.6 μm and 4.5 μm images produced through Pipelines 1 and 2, respectively. From these profiles, we derive the following integrated quantities: total magnitudes, stellar masses, concentration parameters, and galaxy size metrics. We showcase NIR scaling relations for ETGs among these quantities. Results. We examine general trends across the whole S 4 G and ETG extension among our derived parameters, highlighting differences between ETGs and late-type galaxies (LTGs). The latter are, on average, more massive and more concentrated than LTGs, and subtle distinctions are seen among ETG morphological subtypes. We also derive the following scaling relations and compare them with previous results in visible light: mass-size (both half-light and isophotal), mass-concentration, mass-surface brightness (central, effective, and within 1 kpc), and mass-color. Conclusions. We find good agreement with previous works, though some relations (e.g., mass-central surface brightness) will require more careful multicomponent decompositions to be fully understood. The relations between mass and isophotal radius and between mass and surface brightness within 1 kpc, in particular, show notably small scatter. The former provides important constraints on the limits of size growth in galaxies, possibly related to star formation thresholds, while the latter–particularly when paired with the similarly tight relation for LTGs–showcases the striking self-similarity of galaxy cores, suggesting they evolve little over cosmic time. All of the profiles and parameters described in this paper will be provided to the community via the NASA/IPAC database on a dedicated website.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2022
Admission routes1
Has abstractyes

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