CLEAR: The Evolution of Spatially Resolved Star Formation in Galaxies between 0.5 ≲ z ≲ 1.7 Using Hα Emission Line Maps
Bibliographic record
Abstract
Abstract Using spatially resolved H α emission line maps of star-forming galaxies, we study the spatial distribution of star formation over a wide range in redshift (0.5 ≲ z ≲ 1.7). Our z ∼ 0.5 measurements come from deep Hubble Space Telescope (HST) Wide Field Camera 3 G102 grism spectroscopy obtained as part of the CANDELS Ly α Emission at Reionization Experiment. For star-forming galaxies with log( M * / M ⊙ ) ≥ 8.96, the mean H α effective radius is 1.2 ± 0.1 times larger than that of the stellar continuum, implying inside-out growth via star formation. This measurement agrees within 1 σ with those measured at z ∼ 1 and z ∼ 1.7 from the 3D-HST and KMOS 3D surveys, respectively, implying no redshift evolution. However, we observe redshift evolution in the stellar mass surface density within 1 kpc (Σ 1kpc ). Star-forming galaxies at z ∼ 0.5 with a stellar mass of log( M * / M ⊙ ) = 9.5 have a ratio of Σ 1kpc in H α relative to their stellar continuum that is lower by (19 ± 2)% compared to z ∼ 1 galaxies. Σ 1kpc,H α /Σ 1kpc,Cont decreases toward higher stellar masses. The majority of the redshift evolution in Σ 1kpc,H α /Σ 1kpc,Cont versus stellar mass stems from the fact that log(Σ 1kpc,H α ) declines twice as much as log(Σ 1kpc,Cont ) from z ∼ 1 to 0.5 (at a fixed stellar mass of log( M * / M ⊙ ) = 9.5). By comparing our results to the TNG50 cosmological magneto-hydrodynamical simulation, we rule out dust as the driver of this evolution. Our results are consistent with inside-out quenching following in the wake of inside-out growth, the former of which drives the significant drop in Σ 1kpc,H α from z ∼ 1 to z ∼ 0.5.
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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 | 0.001 |
| 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".