Dwarf galaxies in the MATLAS survey: Hubble Space Telescope observations of nuclear star clusters
Bibliographic record
Abstract
In dwarf galaxies, nuclear star clusters (NSCs) are believed to primarily form from the migration and merger of globular clusters (GCs), with a possible contribution from in situ star-forming activity triggered by gas infall. We present the study of NSCs in 41 MATLAS survey dwarf galaxies including ultra-diffuse galaxies (UDGs), as part of a large follow-up imaging program with the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) using the F606W and F814W filters. The sample is biased toward low surface brightness and large dwarfs, i.e., UDG-like galaxies, and includes two galaxies with a double nucleus; 13 newly identified nucleated dwarfs, thanks to HST’s high spatial resolution; and five candidate ultra-compact dwarf progenitors. We modeled the NSCs with a Sérsic profile and derived their structural properties and photometry. We find the NSC Sérsic index to increase with the luminosity and stellar mass, while no obvious trend is seen for the effective radius and ellipticity. The faint NSCs tend to have a constant color profile, whereas the bright ones have a bluer center, suggesting that the most massive NSCs in our sample might have experienced a mixed formation scenario, including in situ star formation. A significant portion of our NSCs tend to be more massive than for other galaxy samples of similar stellar mass, which could be due to some dwarfs undergoing tidal disruption or an initial formation of massive NSCs from multiple GC mergers and in situ star-forming activity. More observations of resolved NSCs are needed to be able to infer their formation scenario from the structural properties and photometry in dwarfs.
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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 | 0.001 |
| 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".