AN ALMA SURVEY OF SUBMILLIMETER GALAXIES IN THE EXTENDED CHANDRA DEEP FIELD SOUTH: NEAR-INFRARED MORPHOLOGIES AND STELLAR SIZES
Bibliographic record
Abstract
We analyze Hubble Space Telescope WFC3/ H 160 -band observations of a sample of 48 Atacama Large Millimeter/submillimeter Array detected submillimeter galaxies (SMGs) in the Extended Chandra Deep Field South field, to study their stellar morphologies and sizes. We detect 79% ± 17% of the SMGs in the H 160 -band imaging with a median sensitivity of 27.8 mag, and most (80%) of the nondetections are SMGs with 870 μm fluxes of S 870 < 3 mJy. With a surface brightness limit of μ H ∼ 26 mag arcsec −2 , we find that 82% ± 9% of the H 160 -band-detected SMGs at z = 1–3 appear to have disturbed morphologies, meaning they are visually classified as either irregulars or interacting systems, or both. By determining a Sérsic fit to the H 160 surface brightness profiles, we derive a median Sérsic index of n = 1.2 ± 0.3 and a median half-light radius of r e = 4.4 kpc for our SMGs at z = 1–3. We also find significant displacements between the positions of the H 160 component and 870 μm emission in these systems, suggesting that the dusty starburst regions and less-obscured stellar distribution are not colocated. We find significant differences in the sizes and the Sérsic index between our z = 2–3 SMGs and z ∼ 2 quiescent galaxies, suggesting that a major transformation of the stellar light profile is needed in the quenching processes if SMGs are progenitors of the red-and-dead z ∼ 2 galaxies. Given the short-lived nature of SMGs, we postulate that the majority of the z = 2–3 SMGs with S 870 ≳ 2 mJy are early/mid-stage major mergers.
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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 | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".