Clusters, Clumps, Dust, and Gas (CCDG) in NGC 1614: Benchmarking Cluster Demographics in Extreme Systems
Bibliographic record
Abstract
Abstract Observations of young star clusters in a variety of galaxies have been used to constrain basic properties related to star formation, such as the fraction of stars found in clusters (Γ) and the shape of the cluster mass function (CMF). However, the results can depend heavily on the reliability of the cluster age-dating process and other assumptions. One of the biggest challenges for successful age-dating lies in breaking the age–reddening degeneracy, where older, dust-free clusters and young, reddened clusters can have similar broadband colors. While this degeneracy affects cluster populations in all galaxies, it is particularly challenging in systems with dusty, extreme star-forming environments. We study the cluster demographics in the luminous infrared galaxy NGC 1614 using Hubble Space Telescope imaging taken in eight optical–near-infrared passbands. For age-dating, we adopt a spectral energy distribution fitting process that limits the maximum allowed reddening by region and includes Hα photometry directly. We find that without these assumptions essentially all clusters in the dust-free UV-bright arm that should have ages ≈50–250 Myr are incorrectly assigned ages younger than 10 Myr. We find that this method greatly reduces the number of clusters in the youngest (τ < 10 Myr) age bin and shows a fairly uniform distribution of massive clusters, the most massive being ≈few × 107 M ⊙. A maximum likelihood fit shows that the CMF is well fitted by a power law with an index of approximately −1.8, with no statistically significant high-mass cutoff. We calculate the fraction of stars born in clusters to be Γ1−10 = 22.4% ± 5.7%. The fraction of stars in clusters decreases quickly over time, with Γ10−100 = 4.5% ± 1.1% and Γ100−400 = 1.7% ± 0.4%, suggesting that clusters dissolve rapidly over the first ∼0.5 Gyr. The decreasing fraction of stars in clusters is consistent with the declining shape observed for the cluster age distribution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".