The Herschel★ PEP/HerMES luminosity function – I. Probing the evolution of PACS selected Galaxies to z ≃ 4
Bibliographic record
Abstract
We exploit the deep and extended far infrared data sets (at 70, 100 and 160 um) of the Herschel GTO PACS Evolutionary Probe (PEP) Survey, in combination with the HERschel Multi tiered Extragalactic Survey (HerMES) data at 250, 350 and 500 um, to derive the evolution of the restframe 35 um, 60 um, 90 um, and total infrared (IR) luminosity functions (LFs) up to z~4. We detect very strong luminosity evolution for the total IR LF combined with a density evolution. In agreement with previous findings, the IR luminosity density increases steeply to z~1, then flattens between z~1 and z~3 to decrease at z greater than 3. Galaxies with different SEDs, masses and sSFRs evolve in very different ways and this large and deep statistical sample is the first one allowing us to separately study the different evolutionary behaviours of the individual IR populations contributing to the IR luminosity density. Galaxies occupying the well established SFR/stellar mass main sequence (MS) are found to dominate both the total IR LF and luminosity density at all redshifts, with the contribution from off MS sources (0.6 dex above MS) being nearly constant (~20% of the total IR luminosity density) and showing no significant signs of increase with increasing z over the whole 0.8
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".