Revealing the Stellar Mass and Dust Distributions of Submillimeter Galaxies at Redshift 2
Bibliographic record
Abstract
Abstract We combine high-resolution ALMA and HST/CANDELS observations of 20 submillimeter galaxies (SMGs), predominantly from the AS2UDS survey at z ≃ 2, with bright rest-frame optical counterparts ( ) to investigate the resolved structural properties of their dust and stellar components. We derive two-dimensional stellar-mass distributions that are inferred from spatial mass-to-light ratio ( ) corrections based on rest-frame optical colors. Due to the high central column densities of dust in our SMGs, our mass distributions likely represent a lower limit to the true central mass density. The centroid positions between the inferred stellar-mass and the dust distributions agree within 1.1 kpc, indicating an overall good spatial agreement between the two components. The majority of our sources exhibit compact dust configurations relative to the stellar component (with a median ratio of effective radii = 0.6). This ratio does not change with specific star formation rate over the factor of 30 spanned by our targets, sampling the locus of “normal” main-sequence galaxies up to the starburst regime, . Unlike typical spiral galaxies in the local universe, our results imply that massive SMGs are experiencing centrally enhanced star formation. The sizes and stellar densities of our SMGs are in agreement with those of the passive population at z = 1.5, which is consistent with these systems being the descendants of z ≃ 2 SMGs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".