GOODS-<i>HERSCHEL</i>: STAR FORMATION, DUST ATTENUATION, AND THE FIR–RADIO CORRELATION ON THE MAIN SEQUENCE OF STAR-FORMING GALAXIES UP TO<i>z</i>≃ 4
Bibliographic record
Abstract
We use deep panchromatic data sets in the GOODS-N field, from GALEX to the deepest Herschel far-infrared (FIR) and VLA radio continuum imaging, to explore the evolution of star-formation activity and dust attenuation properties of star-forming galaxies to z ≃ 4, using mass-complete samples. Our main results can be summarized as follows: (i) the slope of the star-formation rate– M * correlation is consistent with being constant ≃0.8 up to z ≃ 1.5, while its normalization keeps increasing with redshift; (ii) for the first time we are able to explore the FIR–radio correlation for a mass-selected sample of star-forming galaxies: the correlation does not evolve up to z ≃ 4; (iii) we confirm that galaxy stellar mass is a robust proxy for UV dust attenuation in star-forming galaxies, with more massive galaxies being more dust attenuated. Strikingly, we find that this attenuation relation evolves very weakly with redshift, with the amount of dust attenuation increasing by less than 0.3 mag over the redshift range [0.5–4] for a fixed stellar mass; (iv) the correlation between dust attenuation and the UV spectral slope evolves with redshift, with the median UV slope becoming bluer with redshift. By z ≃ 3, typical UV slopes are inconsistent, given the measured dust attenuations, with the predictions of commonly used empirical laws. (v) Finally, building on existing results, we show that gas reddening is marginally larger (by a factor of around 1.3) than the stellar reddening at all redshifts probed. Our results support a scenario where the ISM conditions of typical star-forming galaxies evolve with redshift, such that at z ≥ 1.5 Main Sequence galaxies have ISM conditions moving closer to those of local starbursts.
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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 | 0.001 |
| 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".