Metallicity Distribution Function of the Eridanus II Ultra-faint Dwarf Galaxy from Hubble Space Telescope Narrowband Imaging
Bibliographic record
Abstract
Abstract We use deep narrowband CaHK (F395N) imaging taken with the Hubble Space Telescope (HST) to construct the metallicity distribution function (MDF) of Local Group ultra-faint dwarf galaxy Eridanus II (Eri II). When combined with archival F475W and F814W data, we measure metallicities for 60 resolved red giant branch stars as faint as m F475W ∼ 24 mag, a factor of ∼4× more stars than current spectroscopic MDF determinations. We find that Eri II has a mean metallicity of [Fe/H] = −2.50 − 0.07 + 0.07 and a dispersion of σ [ Fe / H ] = 0.42 − 0.06 + 0.06 , which are consistent with spectroscopic MDFs, though more precisely constrained owing to a larger sample. We identify a handful of extremely metal-poor star candidates (EMP; [Fe/H] < −3) that are marginally bright enough for spectroscopic follow-up. The MDF of Eri II appears well described by a leaky box chemical evolution model. We also compute an updated orbital history for Eri II using Gaia eDR3 proper motions, and find that it is likely on first infall into the Milky Way. Our findings suggest that Eri II underwent an evolutionary history similar to that of an isolated galaxy. Compared to MDFs for select cosmological simulations of similar mass galaxies, we find that Eri II has a lower fraction of stars with [Fe/H] < −3, though such comparisons should currently be treated with caution due to a paucity of simulations, selection effects, and known limitations of CaHK for EMPs. This study demonstrates the power of deep HST CaHK imaging for measuring the MDFs of UFDs.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".