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Record W2614279747 · doi:10.1093/mnras/stx1182

Gaia 1 and 2. A pair of new Galactic star clusters

2017· article· en· W2614279747 on OpenAlexfundno aff
S. E. Koposov, Vasily Belokurov, G. Torrealba

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilPlanetary Science DivisionScience Mission DirectorateSmithsonian Astrophysical ObservatoryMax-Planck-Institut für AstronomieNational Central UniversitySpace Telescope Science InstituteSimons FoundationQueen's UniversityEuropean CommissionJohns Hopkins UniversityQueen's University BelfastNational Science FoundationEuropean Space AgencyNational Aeronautics and Space AdministrationDurham UniversitySmithsonian Institution
KeywordsPhysicsSkyMilky WaySiriusStar clusterAstronomyAstrophysicsDwarf galaxyGalaxySatelliteCluster (spacecraft)Star (game theory)Stars

Abstract

fetched live from OpenAlex

We present the results of the very first search for faint Milky Way satellites in the Gaia data. Using stellar positions only, we are able to re-discover objects detected in much deeper data as recently as the last couple of years. While we do not identify new prominent ultrafaint dwarf galaxies, we report the discovery of two new star clusters, Gaia 1 and Gaia 2. Gaia 1 is particularly curious, as it is a massive (2.2 × 104 M⊙), large (∼9 pc) and nearby (4.6 kpc) cluster, situated 10 arcmin away from the brightest star on the sky, Sirius! Even though this satellite is detected at significance in excess of 10, it was missed by previous sky surveys. We conclude that Gaia possesses powerful and unique capabilities for satellite detection, thanks to its unrivalled angular resolution and highly efficient object classification.

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)0.000
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.217
Teacher spread0.205 · 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

Citations70
Published2017
Admission routes1
Has abstractyes

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