The Y dwarf population with <i>HST</i>: unlocking the secrets of our coolest neighbours – II. Parallaxes and proper motions
Bibliographic record
Abstract
ABSTRACT We present astrometric results from a Hubble Space Telescope (HST) campaign aimed at determining precise distances for cold Y-type brown dwarfs. Combining observations from a dedicated HST/WFC3 programme with archival data, we derive astrometric solutions for 15 nearby Y dwarfs, by linking the high-precision relative astrometry from Hubble to the high-accuracy Gaia DR3 absolute reference system, using stars present in both to anchor the two frames of reference. We reach uncertainties on parallaxes below the 1-mas level for half of the sample, and down to 3 mas for two-thirds of the targets, or relative precisions <1 per cent in most cases and 2–5$\times$ improvements over previous measurements. For the remaining targets, we achieved slightly lower precisions on parallaxes (5–12 mas, 5–10 per cent), correlated with the lower signal-to-noise of the faintest targets. The precision reached in our derived proper motions is around 0.1–0.4 mas yr−1 for most targets, and up to 1–2 mas yr−1 for less precise cases. Our estimated parallaxes and proper motions are generally in good agreement with literature values, and consistent to 1–2$\sigma$ with recent Spitzer-derived parallaxes in most cases. These new astrometric solutions provide important validation of these objects’ distances and sky motions, especially given the large disparities seen in previous estimates. Our results demonstrate the power of HST combined with Gaia to measure highly precise absolute astrometry for faint brown dwarfs, and highlights the limitations reached for the reddest and coldest objects, for which JWST will certainly provide a favourable platform to improve these results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".