Multiwavelength Study of Massive Galaxies at<i>z</i>∼2. I. Star Formation and Galaxy Growth
Bibliographic record
Abstract
Examining a sample of massive galaxies at 1.4 < z < 2.5 with K Vega < 22 from GOODS, we compare photometry from Spitzer at mid- and far-IR to submillimeter, radio, and rest-frame UV wavelengths, to test the agreement between different tracers of star formation rates (SFRs) and to explore the implications for galaxy assembly. For z ~ 2 galaxies with moderate luminosities ( L 8 μm < 10 11 L ☉ ), we find that the SFR can be estimated consistently from the multiwavelength data based on local luminosity correlations. However, 20%-30% of massive galaxies, and nearly all those with L 8 μm > 10 11 L ☉ , show a mid-IR excess that is likely due to the presence of obscured active nuclei, as shown in a companion paper. There is a tight and roughly linear correlation between stellar mass and SFR for 24 μm-detected galaxies. For a given mass, the SFR at z = 2 was larger by a factor of ~4 and ~30 relative to that in star-forming galaxies at z = 1 and 0, respectively. Typical ultraluminous infrared galaxies (ULIRGs) at z = 2 are relatively "transparent" to ultraviolet light, and their activity is long lived (≳400 Myr), unlike that in local ULIRGs and high-redshift submillimeter-selected galaxies. ULIRGs are the common mode of star formation in massive galaxies at z = 2, and the high duty cycle suggests that major mergers are not the dominant trigger for this activity. Current galaxy formation models underpredict the normalization of the mass-SFR correlation by about a factor of 4 and the space density of ULIRGs by an order of magnitude but give better agreement for z > 1.4 quiescent galaxies.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".