Hubble Space Telescope Observations of Two Faint Dwarf Satellites of Nearby LMC Analogs from MADCASH*
Bibliographic record
Abstract
Abstract We present a deep Hubble Space Telescope (HST) imaging study of two dwarf galaxies in the halos of Local Volume Large Magellanic Cloud (LMC) analogs. These dwarfs were discovered as part of our Subaru+Hyper Suprime-Cam MADCASH survey: MADCASH-1 is a satellite of NGC 2403 (D ∼ 3.2 Mpc), and MADCASH-2 is a previously unknown dwarf galaxy near NGC 4214 (D ∼ 3 Mpc). Our HST data reach >3.5 mag below the tip of the red giant branch (TRGB) of each dwarf, allowing us to derive their structural parameters and assess their stellar populations. We measure TRGB distances ( D MADCASH − 1 = 3.41 − 0.23 + 0.24 Mpc, D MADCASH − 2 = 3.00 − 0.15 + 0.13 Mpc), and confirm the dwarfs’ associations with their host galaxies. MADCASH-1 is a predominantly old, metal-poor stellar system (age ∼13.5 Gyr, [M/H] ∼ −2.0), similar to many Local Group dwarfs. Modelling of MADCASH-2's color–magnitude diagram suggests that it contains mostly ancient, metal-poor stars (age ∼13.5 Gyr, [M/H] ∼ −2.0), but that ∼10% of its stellar mass was formed 1.1–1.5 Gyr ago and ∼1% was formed 400–500 Myr ago. Given its recent star formation, we search MADCASH-2 for neutral hydrogen using the Green Bank Telescope, but find no emission and estimate an upper limit on the H i mass of <4.8 × 104 M ⊙. These are the faintest dwarf satellites known around host galaxies of LMC mass outside the Local Group (M V,MADCASH−1 = −7.81 ± 0.18, M V,MADCASH−2 = −9.15 ± 0.12), and one of them shows signs of recent environmental quenching by its host. Once the MADCASH survey for faint dwarf satellites is complete, our census will enable us to test predictions from cold dark matter models for hierarchical structure formation and discover the physical mechanisms by which low-mass hosts influence the evolution of their satellites.
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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 | 0.000 |
| 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 | 0.000 |
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".