Stellar masses of clumps in gas-rich, turbulent disc galaxies
Bibliographic record
Abstract
ABSTRACT In this paper, we use Hubble Space Telescope/WFC3 observations of six galaxies from the DYnamics of Newly Assembled Massive Object (DYNAMO) survey, combined with stellar population modelling of the SED, to determine the stellar masses of DYNAMO clumps. The DYNAMO sample has been shown to have properties similar to z ≈ 1.5 turbulent, clumpy discs. DYNAMO sample clump masses offer a useful comparison for studies of z > 1 in that the galaxies have the same properties, yet the observational biases are significantly different. Using DYNAMO, we can more easily probe rest-frame near-IR wavelengths and also probe finer spatial scales. We find that the stellar mass of DYNAMO clumps is typically 107−108M⊙. We employ a technique that makes non-parametric corrections in removal of light from nearby clumps, and carries out a locally determined disc subtraction. The process of disc subtraction is the dominant effect, and can alter clump masses at the 0.3 dex level. Using these masses, we investigate the stellar mass function (MF) of clumps in DYNAMO galaxies. DYNAMO stellar MFs follow a declining power law with slope α ≈ −1.4, which is slightly shallower than, but similar to what is observed in z > 1 lensed galaxies. We compare DYNAMO clump masses to results of simulations. The masses and galactocentric position of clumps in DYNAMO galaxies are more similar to long-lived clumps in simulations. Similar to recent DYNAMO results on the stellar population gradients, these results are consistent with simulations that do not employ strong ‘early’ radiative feedback prescriptions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".