Tracing the evolution of dust-obscured activity using sub-millimetre galaxy populations from STUDIES and AS2UDS
Bibliographic record
Abstract
ABSTRACT We analyse the physical properties of 121 SNR ≥ 5 sub-millimetre galaxies (SMGs) from the STUDIES 450 μm survey. We model their UV-to-radio spectral energy distributions using magphys+photo-z and compare the results to similar modelling of 850 μm-selected SMG sample from AS2UDS, to understand the fundamental physical differences between the two populations at the observed depths. The redshift distribution of the 450-μm sample has a median of z = 1.85 ± 0.12 and can be described by strong evolution of the far-infrared luminosity function. The fainter 450-μm sample has ∼14 times higher space density than the brighter 850-μm sample at z ≲ 2, and a comparable space density at z = 2–3, before rapidly declining, suggesting LIRGs are the main obscured population at z ∼ 1–2, while ULIRGs dominate at higher redshifts. We construct rest-frame ∼180-μm-selected and dust-mass-matched samples at z = 1–2 and z = 3–4 from the 450 and 850-μm samples, respectively, to probe the evolution of a uniform sample of galaxies spanning the cosmic noon era. Using far-infrared luminosity, dust masses, and an optically thick dust model, we suggest that higher redshift sources have higher dust densities due to inferred dust continuum sizes which are roughly half of those for the lower redshift population at a given dust mass, leading to higher dust attenuation. We track the evolution in the cosmic dust mass density and suggest that the dust content of galaxies is governed by a combination of both the variation of gas content and dust destruction time-scale.
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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 | 0.001 | 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".