Extended star-forming regions within galaxies in a dense proto-cluster core at <i>z</i> = 2.53
Bibliographic record
Abstract
Abstract At z ∼ 2, star formation activity is thought to be high even in high-density environments such as galaxy clusters and proto-clusters. One of the critical but outstanding issues is if the structural growth of star-forming galaxies can differ depending on their surrounding environments. In order to investigate how galaxies grow their structures and what physical processes are involved in the evolution of galaxies, one requires spatially resolved images of not only stellar components but also star-forming regions within galaxies. We conducted (Adaptive Optics) AO-assisted imaging observations for star-forming galaxies in a dense proto-cluster core at z = 2.53 with IRCS and AO188 mounted on the Subaru Telescope. A combination of AO and narrow-band filters allows us to obtain resolved maps of Hα-emitting regions with an angular resolution of ${0{^{\prime\prime}_{.}}1}$–${0{^{\prime\prime}_{.}}2}$, which corresponds to ∼1 kpc at z ∼ 2.5. Based on stacking analyses, we compare radial profiles of star-forming regions and stellar components and find that the star-forming region of a sub-sample with log (M*/$M_\odot$) ∼ 10–11 is more extended than the stellar component, indicating the inside-out growth of the structure. This trend is similar to the one for star-forming galaxies in general fields at z = 2–2.5 obtained with the same observational technique. Our results suggest that the structural evolution of star-forming galaxies at z = 2–2.5 is mainly driven by internal secular processes irrespective of surrounding environments.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".