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Record W4404300899 · doi:10.3847/1538-3881/ad7de7

Clusters, Clumps, Dust, and Gas (CCDG) in NGC 1614: Benchmarking Cluster Demographics in Extreme Systems

2024· article· en· W4404300899 on OpenAlexaff
Miranda Caputo, Rupali Chandar, Angus Mok, Sean T. Linden, Paul Goudfrooij, Bradley C. Whitmore

Bibliographic record

VenueThe Astronomical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsPhysicsAstrophysicsStar clusterAstronomyGalaxyStar formationPhotometry (optics)Galaxy clusterCluster (spacecraft)Infrared excessSpectral energy distributionInitial mass functionStars

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.214
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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