Stellar and Dust Properties of a Complete Sample of Massive Dusty Galaxies at 1 ≤ <i>z</i> ≤ 4 from MAGPHYS Modeling of UltraVISTA DR3 and <i>Herschel</i> Photometry
Bibliographic record
Abstract
We investigate the stellar and dust properties of massive (log M * / M ⊙ ≥ 10.5) and dusty ( A V ≥ 1) galaxies at 1 ≤ z ≤ 4 by modeling their spectral energy distributions (SEDs) obtained from the combination of UltraVISTA DR3 photometry and Herschel PACS-SPIRE data using MAGPHYS. Although the rest-frame U – V versus V – J ( UVJ ) diagram traces the star formation rates (SFRs) and dust obscuration ( A V ) well out to z ∼ 3, ∼15%–20% of the sample surprisingly resides in the quiescent region of the UVJ diagram, while ∼50% at 3 < z < 4 falls in the unobscured star-forming region. The median SED of massive dusty galaxies exhibits weaker MIR and UV emission, and redder UV slopes with increasing cosmic time. The IR emission for our sample has a significant contribution (>20%) from dust heated by evolved stellar populations rather than star formation, demonstrating the need for panchromatic SED modeling. The local relation between dust mass and SFR is followed only by a subsample with cooler dust temperatures, while warmer objects have reduced dust masses at a given SFR. Most star-forming galaxies in our sample do not follow local IRX– β relations, though IRX does strongly correlate with A V . Our sample follows local relations, albeit with large scatter, between ISM diagnostics and sSFR. We show that FIR-detected sources represent the extreme of a continuous population of dusty galaxies rather than a fundamentally different population. Finally, using commonly adopted relations to derive SFRs from the combination of the rest-frame UV and the observed 24 μ m is found to overestimate the SFR by a factor of 3–5 for the galaxies in our sample.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".