Stellar Metallicities and Gradients in the Isolated, Quenched Low-mass Galaxy Tucana
Bibliographic record
Abstract
Abstract We measure the metallicities of 374 red giant branch (RGB) stars in the isolated, quenched dwarf galaxy Tucana using Hubble Space Telescope narrowband (F395N) calcium H and K imaging. Our sample is a factor of ∼7 larger than what is available from previous studies. Our main findings are as follows. (i) A global metallicity distribution function (MDF) with 〈 [Fe/H] 〉 = − 1.55 − 0.04 + 0.04 and σ [Fe/H] = 0.54 − 0.03 + 0.03 . (ii) A metallicity gradient of −0.54 ± 0.07 dex R e − 1 (−2.1 ± 0.3 dex kpc−1) over the extent of our imaging (∼2.5 R e ), which is steeper than literature measurements. Our finding is consistent with predicted gradients from the publicly available FIRE-2 simulations, in which bursty star formation creates stellar population gradients and dark matter cores. (iii) Tucana’s bifurcated RGB has distinct metallicities: a blue RGB with 〈 [Fe/H] 〉 = − 1.78 − 0.06 + 0.06 and σ [Fe/H] = 0.44 − 0.06 + 0.07 and a red RGB with 〈 [Fe/H] 〉 = − 1.08 − 0.07 + 0.07 and σ [Fe/H] = 0.42 − 0.06 + 0.06 . (iv) At fixed stellar mass, Tucana is more metal-rich than Milky Way satellites by ∼0.4 dex, but its blue RGB is chemically comparable to the satellites. Tucana’s MDF appears consistent with star-forming isolated dwarfs, though MDFs of the latter are not as well populated. (v) About 2% of Tucana’s stars have [Fe/H] < −3% and 20% have [Fe/H] > −1. We provide a catalog for community spectroscopic follow-up.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".