Evolution of cosmic star formation in the SCUBA-2 Cosmology Legacy Survey
Bibliographic record
Abstract
We present a new exploration of the cosmic star formation history and dust obscuration in massive galaxies at redshifts 0.5 < z < 6. We utilize the deepest 450- and 850-μm imaging from SCUBA-2 CLS, covering 230 arcmin2 in the AEGIS, COSMOS and UDS fields, together with 100–250 μm imaging from Herschel. We demonstrate the capability of the t-phot deconfusion code to reach below the confusion limit, using multiwavelength prior catalogues from CANDELS/3D-HST. By combining IR and UV data, we measure the relationship between total star formation rate (SFR) and stellar mass up to z ∼ 5, indicating that UV-derived dust corrections underestimate the SFR in massive galaxies. We investigate the relationship between obscuration and the UV slope (the IRX–β relation) in our sample, which is similar to that of low-redshift starburst galaxies, although it deviates at high stellar masses. Our data provide new measurements of the total SFR density (SFRD) in |$M_{\ast }>10^{10}\,\textrm{M}_{\odot }$| galaxies at 0.5 < z < 6. This is dominated by obscured star formation by a factor of >10. One third of this is accounted for by 450-μm-detected sources, while one-fifth is attributed to UV-luminous sources (brighter than |$L_{\rm UV}^\ast$|), although even these are largely obscured. By extrapolating our results to include all stellar masses, we estimate a total SFRD that is in good agreement with previous results from IR and UV data at z ≲ 3, and from UV-only data at z ∼ 5. The cosmic star formation history undergoes a transition at z ∼ 3–4, as predominantly unobscured growth in the early Universe is overtaken by obscured star formation, driven by the build-up of the most massive galaxies during the peak of cosmic assembly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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) | 0.001 | 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".