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Record W3102253569 · doi:10.3847/2041-8213/abcb83

Discovery of Magellanic Stellar Debris in the H3 Survey

2020· article· en· W3102253569 on OpenAlexaff
Dennis Zaritsky, Charlie Conroy, Rohan P. Naidu, Phillip A. Cargile, M. E. Putman, Gurtina Besla, Ana Bonaca, Nelson Caldwell, J. Han, Benjamin D. Johnson, Joshua S. Speagle, Yuan-Sen Ting

Bibliographic record

VenueThe Astrophysical Journal Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsPhysicsMilky WayStarsAstrophysicsLarge Magellanic CloudAstronomySmall Magellanic Cloud

Abstract

fetched live from OpenAlex

Abstract We report the discovery of 15 stars in the H3 survey that lie, in projection, near the tip of the trailing gaseous Magellanic Stream (MS). The stars have Galactocentric velocities <−155 km s−1, Galactocentric distances of ≈40 to 80 kpc (increasing along the MS), and [Fe/H] consistent with that of stars in the Small Magellanic Cloud. These 15 stars comprise 94% (15 of 16) of the H3 observed stars to date that have R GAL > 37.5 kpc, −350 km s−1 < V GSR < −155 km s−1, and are not associated with the Sagittarius Stream. They represent a unique portion of the Milky Way’s outer halo phase space distribution function and confirm that unrelaxed structure is detectable even at radii where H3 includes only a few hundred stars. Due to their statistical excess, their close association with the MS and HI compact clouds in the same region, both in position and velocity space, and their plausible correspondence with tidal debris in a published simulation, we identify these stars as debris of past Magellanic Cloud encounters. These stars are evidence for a stellar component of the tidal debris field far from the Clouds themselves and provide unique constraints on the interaction.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.017
GPT teacher head0.218
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

Citations19
Published2020
Admission routes1
Has abstractyes

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