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Record W4294021964 · doi:10.1088/1674-4527/ac8b5b

The Stellar Abundances and Galactic Evolution Survey: Photonic Passbands and Extinction Coefficients for the <i>u</i> and <i>v</i> Bands

2022· article· en· W4294021964 on OpenAlexfundno aff
Kefeng Tan, Gang Zhao, Zhou Fan, Wei Wang, Haibo Yuan, Jie Zheng, Chun Li, Nan Song, Jingkun Zhao

Bibliographic record

VenueResearch in Astronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersLos Alamos National LaboratoryNational Astronomical Observatories, Chinese Academy of SciencesSmithsonian Astrophysical ObservatoryMax-Planck-Institut für AstronomieNational Development and Reform CommissionEötvös Loránd TudományegyetemQueen's UniversityChinese Academy of SciencesPlanetary Science DivisionScience Mission DirectorateNational Natural Science Foundation of ChinaSpace Telescope Science InstituteNational Central UniversityGordon and Betty Moore FoundationJohns Hopkins UniversityQueen's University BelfastNational Science FoundationEuropean Space AgencyNational Aeronautics and Space AdministrationDurham UniversitySmithsonian Institution
KeywordsPhysicsExtinction (optical mineralogy)Photometry (optics)AstrophysicsPhotometric systemGalaxyAstronomyStarsOptics

Abstract

fetched live from OpenAlex

Abstract The Stellar Abundances and Galactic Evolution Survey (SAGES) is a multi-band photometric survey focused on estimation of stellar atmospheric parameters and interstellar extinction. In this paper we have derived photonic passbands for the intermediate-band u and v filters of the SAGES photometric system. The derived photonic passbands have been compared with those of the u and v filters of the Strömgren and SkyMapper systems. Synthetic photometry based on the derived photonic passbands could reproduce the observations very well. We have also derived observed, model-free extinction coefficients for the SAGES u and v bands (as well as the Pan-STARRS grizy bands) using the “standard pair” method. The derived reddening coefficients have been compared with those predicted by the extinction laws. Variations of reddening coefficients with effective temperatures and color excesses of B – V given by Schlegel et al. ( E ( B − V ) SFD ) have been investigated. No obvious trends or significant variations with effective temperatures have been found, but reddening coefficients for all the colors exhibit declining trends with increasing E ( B − V ) SFD , with typical relative variations of twenty-some percent from E ( B − V ) SFD ∼ 0 to 1.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.286
Teacher spread0.256 · 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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