The Stellar Populations of Two Ultra-diffuse Galaxies from Optical and Near-infrared Photometry
Bibliographic record
Abstract
Abstract We present observational constraints on the stellar populations of two ultra-diffuse galaxies (UDGs) using optical through near-infrared (NIR) spectral energy distribution (SED) fitting. Our analysis is enabled by new Spitzer-IRAC 3.6 and 4.5 μm imaging, archival optical imaging, and the prospector fully Bayesian SED fitting framework. Our sample contains one field UDG (DGSAT I), one Virgo cluster UDG (VCC 1287), and one Virgo cluster dwarf elliptical for comparison (VCC 1122). We find that the optical–NIR colors of the three galaxies are significantly different from each other. We infer that VCC 1287 has an old (≳7.7 Gyr) and surprisingly metal-poor ([Z/Z ⊙] ≲ −1.0) stellar population, even after marginalizing over uncertainties on diffuse interstellar dust. In contrast, the field UDG DGSAT I shows evidence of being younger than the Virgo UDG, with an extended star formation history and an age posterior extending down to ∼3 Gyr. The stellar metallicity of DGSAT I is sub-solar but higher than that of the Virgo UDG, with in the case of exactly zero diffuse interstellar dust, DGSAT I may even have solar metallicity. With VCC 1287 and several Coma UDGs, a general picture is emerging where cluster UDGs may be “failed” galaxies, but the field UDG DGSAT I seems more consistent with a stellar feedback-induced expansion scenario. In the future, our approach can be applied to a large and diverse sample of UDGs down to faint surface brightness limits, with the goal of constraining their stellar ages, stellar metallicities, and circumstellar and diffuse interstellar dust content.
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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 | 0.000 |
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".