Resolved Kennicutt–Schmidt law in two strongly lensed star-forming galaxies at redshift 1
Bibliographic record
Abstract
We study the star formation rate (SFR) vs. molecular gas mass (Mmol) scaling relation from hundreds to thousands of parsec in two strongly lensed galaxies at redshift z ∼ 1, the Cosmic Snake and A521. We trace the SFR using extinction-corrected rest-frame UV observations with the Hubble Space Telescope (HST), and Mmol using detections of the CO(4–3) line with the Atacama Large Millimeter/submillimeter Array (ALMA). The similar angular resolutions of our HST and ALMA observations of 0.15 − 0.2″ combined with magnifications reaching μ > 20 enable us to resolve structures in the galaxies of sizes lower than 100 pc. These resolutions are close to those of studies of nearby galaxies. This allows us to investigate for the first time the Kennicutt–Schmidt (KS) law (SFR–Mmol surface densities) at different spatial scales, from galactic scales to ∼100 pc scales, in galaxies at z ∼ 1. At integrated scales we find that both galaxies satisfy the KS law defined by galaxies at redshifts between 1 and 2.5. We test the resolved KS (rKS) law in cells of sizes down to 200 pc in the two galaxies. We observe that this relationship generally holds in these z ∼ 1 galaxies, although its scatter increases significantly with decreasing spatial scales. We check the scale dependence of the spatial correlation between the surface densities of SFR and Mmol by focusing on apertures centred on individual star-forming regions and molecular clouds. We conclude that star-forming regions and molecular clouds become spatially de-correlated at ≲1 kpc in the Cosmic Snake, whereas they appear de-correlated at all spatial scales (from 400 pc to 6 kpc) in A521.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".