Evolution of dust temperature of galaxies through cosmic time as seen by Herschel
Bibliographic record
Abstract
We study the dust properties of galaxies in the redshift range 0.1 ≲z≲ 2.8 observed by the Herschel Space Observatory in the field of the Great Observatories Origins Deep Survey-North as part of the PACS Extragalactic Probe (PEP) and Herschel Multi-tiered Extragalactic Survey (HerMES) key programmes. Infrared (IR) luminosity (L) and dust temperature (T) of galaxies are derived from the spectral energy distribution fit of the far-IR (FIR) flux densities obtained with the PACS and SPIRE instruments onboard Herschel. As a reference sample, we also obtain IR luminosities and dust temperatures of local galaxies at z < 0.1 using AKARI and IRAS data in the field of the Sloan Digital Sky Survey. We compare the L-T relation between the two samples and find that the median T of Herschel-selected galaxies at z< 0.5 with L≲ 5 × 10L appears to be 2-5 K colder than that of AKARI-selected local galaxies with similar luminosities, and the dispersion in T for high-z galaxies increases with L due to the existence of cold galaxies that are not seen among local galaxies. We show that this large dispersion of the L-T relation can bridge the gap between local star-forming galaxies and high-z submillimetre galaxies (SMGs). We also find that three SMGs with very low T (≲20 K) covered in this study have close neighbouring sources with similar 24-μm brightness, which could lead to an overestimation of FIR/(sub)millimetre fluxes of the SMGs. © 2010 The Authors. Journal compilation
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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 | 0.002 |
| 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 | 0.000 |
| 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".