KILOPARSEC-SCALE DUST DISKS IN HIGH-REDSHIFT LUMINOUS SUBMILLIMETER GALAXIES
Bibliographic record
Abstract
ABSTRACT We present high-resolution (0.″16) 870 μ m Atacama Large Millimeter/submillimeter Array (ALMA) imaging of 16 luminous ( ) submillimeter galaxies (SMGs) from the ALESS survey of the Extended Chandra Deep Field South. This dust imaging traces the dust-obscured star formation in these galaxies on ∼1.3 kpc scales. The emission has a median effective radius of R e = 0.″24 ± 0.″02, corresponding to a typical physical size of 1.8 ± 0.2 kpc. We derive a median Sérsic index of n = 0.9 ± 0.2, implying that the dust emission is remarkably disk-like at the current resolution and sensitivity. We use different weighting schemes with the visibilities to search for clumps on 0.″12 (∼1.0 kpc) scales, but we find no significant evidence for clumping in the majority of cases. Indeed, we demonstrate using simulations that the observed morphologies are generally consistent with smooth exponential disks, suggesting that caution should be exercised when identifying candidate clumps in even moderate signal-to-noise ratio interferometric data. We compare our maps to comparable-resolution Hubble Space Telescope -band images, finding that the stellar morphologies appear significantly more extended and disturbed, and suggesting that major mergers may be responsible for driving the formation of the compact dust disks we observe. The stark contrast between the obscured and unobscured morphologies may also have implications for SED fitting routines that assume the dust is co-located with the optical/near-IR continuum emission. Finally, we discuss the potential of the current bursts of star formation to transform the observed galaxy sizes and light profiles, showing that the descendants of these SMGs are expected to have stellar masses, effective radii, and gas surface densities consistent with the most compact massive ( 1–2 × 10 11 ) early-type galaxies observed locally.
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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) | 0.001 | 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".