The effect of spatial resolution on optical and near-IR studies of stellar clusters: implications for the origin of the red excess
Bibliographic record
Abstract
Recent ground-based near-IR (NIR) studies of stellar clusters in nearby galaxies have suggested that young clusters remain embedded for 7–10 Myr in their progenitor molecular cloud, in conflict with optical-based studies which find that clusters are exposed after 1–3 Myr. Here, we investigate the role that spatial resolution plays in this apparent conflict. We use a recent catalogue of young (<10 Myr) massive (>5000 M⊙) clusters in the nearby spiral galaxy, M83, along with Hubble Space Telescope (HST) imaging in the optical and NIR, and ground-based NIR imaging, to see how the colours (and hence estimated properties such as age and extinction) are affected by the aperture size employed, in order to simulate studies of differing resolution. We find that the NIR is heavily affected by the resolution, and when aperture sizes >40 pc are used, all young/blue clusters move redwards in colour space, which results in their appearance as heavily extincted clusters. However, this is due to contamination from nearby sources and nebular emission, and is not an extinction effect. Optical colours are much less affected by resolution. Due to the larger effect of contamination in the NIR, we find that, in some cases, clusters will appear to show NIR excess when large (>20 pc) apertures are used. Our results explain why few young (<6 Myr), low-extinction (|${A_{\rm V}}< 1$| mag) clusters have been found in recent ground-based NIR studies of cluster populations, while many such clusters have been found in higher resolution HST-based studies. Additionally, resolution effects appear to (at least partially) explain the origin of the NIR excess that has been found in a number of extragalactic young massive clusters.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".