Exploring the physical properties of lensed star-forming clumps at 2 ≲ <i>z</i> ≲ 6
Bibliographic record
Abstract
ABSTRACT We study the physical properties (size, stellar mass, luminosity, and star formation rate) and scaling relations for a sample of 166 star-forming clumps with redshift z ∼ 2–6.2. They are magnified by the Hubble Frontier Field galaxy cluster MACS J0416 and have robust lensing magnification (2 ≲ μ ≲ 82) computed by using our high-precision lens model, based on 182 multiple images. Our sample extends by ∼3 times the number of spectroscopically confirmed lensed clumps at z ≳ 2. We identify clumps in ultraviolet continuum images and find that, whenever the effective spatial resolution (enhanced by gravitational lensing) increases, they fragment into smaller entities, likely reflecting the hierarchically organized nature of star formation. Kpc-scale clumps, most commonly observed in field, are not found in our sample. The physical properties of our sample extend the parameter space typically probed by z ≳ 1 field observations and simulations, by populating the low mass (M⋆ ≲ 107 M⊙), low star formation rate (SFR ≲ 0.5 M⊙ yr−1), and small size (Reff ≲ 100 pc) regime. The new domain probed by our study approaches the regime of compact stellar complexes and star clusters. In the mass–size plane, our sample spans the region between galaxies and globular clusters, with a few clumps in the region populated by young star clusters and globular clusters. For the bulk of our sample, we measure star formation rates which are higher than those observed locally in compact stellar systems, indicating different conditions for star formation at high redshift than in the local Universe.
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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 | 0.002 |
| 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 | 0.001 |
| 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".