The Herschel census of infrared SEDs through cosmic time★
Bibliographic record
Abstract
Using Herschel data from the deepest SPIRE and PACS surveys (HerMES and PEP) in COSMOS, GOODS-S and GOODS-N, we examine the dust properties of infrared (IR)-luminous (LIR > 1010 L⊙) galaxies at 0.1 < z < 2 and determine how these evolve with cosmic time. The unique angle of this work is the rigorous analysis of survey selection effects, making this the first study of the star-formation-dominated, IR-luminous population within a framework almost entirely free of selection biases. We find that IR-luminous galaxies have spectral energy distributions (SEDs) with broad far-IR peaks characterized by cool/extended dust emission and average dust temperatures in the 25–45 K range. Hot (T > 45 K) SEDs and cold (T < 25 K), cirrus-dominated SEDs are rare, with most sources being within the range occupied by warm starbursts such as M82 and cool spirals such as M51. We observe a luminosity–temperature (L-T) relation, where the average dust temperature of log [LIR/L⊙] ∼ 12.5 galaxies is about 10 K higher than that of their log [LIR/L⊙] ∼ 10.5 counterparts. However, although the increased dust heating in more luminous systems is the driving factor behind the L-T relation, the increase in dust mass and/or starburst size with luminosity plays a dominant role in shaping it. Our results show that the dust conditions in IR-luminous sources evolve with cosmic time: at high redshift, dust temperatures are on average up to 10 K lower than what is measured locally (z ≲ 0.1). This is manifested as a flattening of the L-T relation, suggesting that (ultra)luminous infrared galaxies [(U)LIRGs] in the early Universe are typically characterized by a more extended dust distribution and/or higher dust masses than local equivalent sources. Interestingly, the evolution in dust temperature is luminosity dependent, with the fraction of LIRGs with T < 35 K showing a two-fold increase from z ∼ 0 to z ∼ 2, whereas that of ULIRGs with T < 35 K shows a six-fold increase. Our results suggest a greater diversity in the IR-luminous population at high redshift, particularly for ULIRGs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.008 | 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".