FMOS near-IR spectroscopy of<i>Herschel</i>-selected galaxies: star formation rates, metallicity and dust attenuation at<i>z</i>∼ 1
Bibliographic record
Abstract
We investigate the properties (e.g. star formation rate, dust attenuation, stellar mass and metallicity) of a sample of infrared (IR) luminous galaxies at z ∼ 1 via near-IR spectroscopy with Subaru-FMOS. Our sample consists of Herschel SPIRE and Spitzer MIPS selected sources in the COSMOS field with photometric redshifts in the range of 0.7 < zphot < 1.8, which have been targeted in two pointings (0.5 deg2) with FMOS. We find a modest success rate for emission-line detections, with candidate Hα emission lines detected for 57 of 168 SPIRE sources (34 per cent). By stacking the near-IR spectra we directly measure the mean Balmer decrement for the Hα and Hβ lines, finding a value of 〈E(B − V)〉 = 0.51 ± 0.27 for 〈LIR〉 = 1012 L⊙ sources at 〈z〉 = 1.36. By comparing star formation rates estimated from the IR and from the dust-uncorrected Hα line we find a strong relationship between dust attenuation and star formation rate. This relation is broadly consistent with that previously seen in star-forming galaxies at z ∼ 0.1. Finally, we investigate the metallicity via the N2 ratio, finding that z ∼ 1 IR-selected sources are indistinguishable from the local mass–metallicity relation. We also find a strong correlation between dust attenuation and metallicity, with the most metal-rich IR sources experiencing the largest levels of dust attenuation.
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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 | 0.000 | 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".