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Record W4224286419 · doi:10.3847/2041-8213/ac79af

Multiplicity Statistics of Stars in the Sagittarius Dwarf Spheroidal Galaxy: Comparison to the Milky Way

2022· article· en· W4224286419 on OpenAlexaff
Victoria Bonidie, Travis Court, Christine Mazzola Daher, Catherine E. Fielder, Carles Badenes, Jeffrey A. Newman, Maxwell Moe, Kaitlin M. Kratter, Matthew G. Walker, Steven R. Majewski, Christian R. Hayes, Sten Hasselquist, Keivan G. Stassun, Marina Kounkel, Don Dixon, Guy S. Stringfellow, Joleen K. Carlberg, Borja Anguiano, Nathan De Lee, T. Nicholas

Bibliographic record

VenueThe Astrophysical Journal Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsPhysicsAstrophysicsMilky WaySagittariusStarsDwarf spheroidal galaxyGalaxyAstronomyDwarf galaxySpiral galaxyInteracting galaxy

Abstract

fetched live from OpenAlex

Abstract We use time-resolved spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) to examine the distribution of radial velocity (RV) variations in 249 stars identified as members of the Sagittarius (Sgr) dwarf spheroidal (dSph) galaxy by Hayes et al. We select Milky Way (MW) stars that have stellar parameters ( log ( g ) , T eff, and [Fe/H] ) similar to those of the Sagittarius members by means of a k-d tree of dimension 3. We find that the shape of the distribution of RV shifts in Sgr dSph stars is similar to that measured in their MW analogs, but the total fraction of RV variable stars in the Sgr dSph is larger by a factor of ∼2. After ruling out other explanations for this difference, we conclude that the fraction of close binaries in the Sgr dSph is intrinsically higher than in the MW. We discuss the implications of this result for the physical processes leading to the formation of close binaries in dwarf spheroidal and spiral galaxies.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.239
Teacher spread0.225 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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