The local far-infrared galaxy colour-luminosity distribution: a reference for BLAST and<i>Herschel</i>/SPIRE submillimetre surveys
Bibliographic record
Abstract
We measure the local galaxy far-infrared (FIR) 60 to 100 μm colour–luminosity distribution using an all-sky IRAS survey. This distribution is an important reference for the next generation of FIR–submillimetre surveys that have and will conduct deep extragalactic surveys at 250–500 μm. With the peak in dust-obscured star-forming activity leading to present-day giant ellipticals now believed to occur in submillimetre galaxies near z∼ 2.5, these new FIR–submillimetre surveys will directly sample the spectral energy distributions of these distant objects at rest-frame FIR wavelengths similar to those at which local galaxies were observed by IRAS. We have taken care to correct for the temperature bias and the evolution effects in our IRAS 60-μm-selected sample. We verify that our colour–luminosity distribution is consistent with the measurements of the local FIR luminosity function, before applying it to the higher redshift Universe. We compare our colour–luminosity correlation with recent dust–temperature measurements of submillimetre galaxies and find evidence for pure luminosity evolution of the form (1 +z)3. This distribution will be useful for the development of evolutionary models for Balloon-borne Large Aperture Submillimeter Telescope (BLAST) and Spectral and Photometric Imaging Receiver (SPIRE) surveys as it provides a statistical distribution of the rest-frame dust temperatures for galaxies as a function of luminosity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".