A direct calibration of the IRX–β relation in Lyman-break Galaxies at z = 3–5
Bibliographic record
Abstract
We use a sample of 4209 Lyman-break galaxies (LBGs) at z ≃ 3, 4, and 5 in the UKIRT Infrared Deep Sky Survey Ultra Deep Survey field to investigate the relationship between the observed slope of the stellar continuum emission in the ultraviolet, β, and the thermal dust emission, as quantified via the so-called ‘infrared excess’ (IRX ≡ LIR/LUV). Through a stacking analysis, we directly measure the 850-|$\mu$|m flux density of LBGs in our deep (0.9 mJy) James Clerk Maxwell Telescope SCUBA-2 850-|${\rm \mu m}$| map as well as deep public Herschel/SPIRE 250-, 350-, and 500-|${\rm \mu m}$| imaging. We establish functional forms for the IRX–β relation to z ∼ 5, confirming that there is no significant redshift evolution of the relation, and that the resulting average IRX–β curve is consistent with a Calzetti-like attenuation law. Comparing our results with recent works in the literature, we confirm that discrepancies in the slope of the IRX–β relation are driven by biases in the methodology used to determine the ultraviolet slopes. Consistent results are found when IRX–β is evaluated by stacking in bins of stellar mass, and we argue that the near-linear IRX–M⋆ relationship is a better proxy for correcting observed ultraviolet luminosities to total star formation rates, provided an accurate handle on M⋆ and also gives clues as to the physical driver of the role of dust-obscured star formation in high-redshift 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".