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Record W4280585046 · doi:10.1051/0004-6361/202243939

The <i>Gaia</i> EDR3 view of Johnson-Kron-Cousins standard stars: the curated Landolt and Stetson collections

2022· article· en· W4280585046 on OpenAlexfundno aff
E. Pancino, P. M. Marrese, S. Marinoni, N. Sanna, Alessio Turchi, M. Tsantaki, M. Rainer, G. Altavilla, M. Monelli, L. Monaco

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersLos Alamos National LaboratoryLawrence Berkeley National LaboratoryAstrophysics DivisionInstitut National de Physique Nucléaire et de Physique des ParticulesAustralian Astronomical Optics-MacquarieDeutsches Elektronen-SynchrotronCarnegie Institution for ScienceInstituto de Astrofísica de CanariasMax-Planck-Institut für AstrophysikLeibniz-Institut für Astrophysik PotsdamNational Development and Reform CommissionEötvös Loránd TudományegyetemNatural Sciences and Engineering Research Council of CanadaNational Central UniversityGordon and Betty Moore FoundationAgenzia Spaziale ItalianaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungYork UniversityMinistério da Ciência, Tecnologia e InovaçãoUniversity of EdinburghQueen's UniversityDeutsche ForschungsgemeinschaftChinese Academy of SciencesLeibniz-GemeinschaftScience Mission DirectorateAgence Nationale de la RechercheAustralian Research CouncilTrinity College DublinUniversity of Colorado BoulderOffice of ScienceMax-Planck-Institut für AstronomieUniversity of OxfordIstituto Nazionale di AstrofisicaDurham UniversityUniversity of Notre DameCarnegie Mellon UniversityUniversity of WashingtonSmithsonian Astrophysical ObservatoryEuropean Space AgencyAlfred P. Sloan FoundationJohns Hopkins UniversityPlanetary Science DivisionCarnegie Institution of WashingtonUniversity of PortsmouthNew Mexico State UniversityUniversity of UtahOhio State UniversityYale UniversityU.S. Department of EnergySmithsonian InstitutionNational Aeronautics and Space AdministrationMacquarie UniversityW. M. Keck FoundationQueen's University BelfastSpace Telescope Science InstituteNational Astronomical Observatories, Chinese Academy of SciencesCalifornia Institute of TechnologyAustralian National UniversityVanderbilt UniversityJavna Agencija za Raziskovalno Dejavnost RSNational Science Foundation
KeywordsStarsPhotometric systemPhotometry (optics)PhysicsVariable (mathematics)Table (database)AstrophysicsAstronomySpace (punctuation)Computer scienceDatabaseMathematics

Abstract

fetched live from OpenAlex

Context. In the era of large surveys and space missions, it is necessary to rely on large samples of well-characterized stars for inter-calibrating and comparing measurements from different surveys and catalogues. Among the most employed photometric systems, the Johnson-Kron-Cousins has been used for decades and for a large amount of important datasets. Aims. Our goal is to profit from the Gaia EDR3 data, Gaia official cross-match algorithm, and Gaia-derived literature catalogues, to provide a well-characterized and clean sample of secondary standards in the Johnson-Kron-Cousins system, as well as a set of transformations between the main photometric systems and the Johnson-Kron-Cousins one. Methods. Using Gaia as a reference, as well as data from reddening maps, spectroscopic surveys, and variable stars monitoring surveys, we curated and characterized the widely used Landolt and Stetson collections of more than 200 000 secondary standards, employing classical as well as machine learning techniques. In particular, our atmospheric parameters agree significantly better with spectroscopic ones, compared to other machine learning catalogues. We also cross-matched the curated collections with the major photometric surveys to provide a comprehensive set of reliable measurements in the most widely adopted photometric systems. Results. We provide a curated catalogue of secondary standards in the Johnson-Kron-Cousins system that are well-measured and as free as possible from variable and multiple sources. We characterize the collection in terms of astrophysical parameters, distance, reddening, and radial velocity. We provide a table with the magnitudes of the secondary standards in the most widely used photometric systems (ugriz, grizy, Gaia, HIPPARCOS, Tycho, 2MASS). We finally provide a set of 167 polynomial transformations, valid for dwarfs and giants, metal-poor and metal-rich stars, to transform UBVRI magnitudes in the above photometric systems and vice-versa.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.008

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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

Citations35
Published2022
Admission routes1
Has abstractyes

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