A<i>Herschel</i>view of the far-infrared properties of submillimetre galaxies
Bibliographic record
Abstract
We study a sample of 61 submillimetre galaxies (SMGs) selected from ground-based surveys, with known spectroscopic redshifts and observed with the Herschel Space Observatory as part of the PACS Evolutionary Probe (PEP) and the Herschel Multi-tiered Extragalactic Survey (HerMES) guaranteed time key programmes. Our study makes use of the broad far-infrared and submillimetre wavelength coverage (100-600 m) only made possible by the combination of observations from the PACS and SPIRE instruments aboard the Herschel Space Observatory. Using a power-law temperature distribution model to derive infrared luminosities and dust temperatures, we measure a dust emissivity spectral index for SMGs of = 2.0 0.2. Our results unambiguously unveil the diversity of the SMG population. Some SMGs exhibit extreme infrared luminosities of 10 13 L and relatively warm dust components, while others are fainter (a few times 10 12 L ) and are biased towards cold dust temperatures. Although at z 2 classical SMGs (>5 mJy at 850 m) have large infrared luminosities (10 13 L ), objects only selected on their submm flux densities (without any redshift informations) probe a large range in dust temperatures and infrared luminosities. The extreme infrared luminosities of some SMGs (L IR 10 12.7 L , 26/61 systems) imply star formation rates (SFRs) of >500 M yr -1 (assuming a Chabrier IMF and no dominant AGN contribution to the FIR luminosity). Such high SFRs are difficult to reconcile with a secular mode of star formation, and may instead correspond to a merger-driven stage in the evolution of these galaxies. Another observational argument in favour of this scenario is the presence of dust temperatures warmer than that of SMGs of lower luminosities (40 K as opposed to 25 K), consistent with observations of local ultraluminous infrared galaxies triggered by major mergers and with results from hydrodynamic simulations of major mergers combined with radiative transfer calculations. Moreover, we find that luminous SMGs are systematically offset from normal star-forming galaxies in the stellar mass-SFR plane, suggesting that they are undergoing starburst events with short duty cycles, compatible with the major merger scenario. On the other hand, a significant fraction of the low infrared luminosity SMGs have cold dust temperatures, are located close to the main sequence of star formation, and therefore might be evolving through a secular mode of star formation. However, the properties of this latter population, especially their dust temperature, should be treated with caution because at these luminosities SMGs are not a representative sample of the entire star-forming galaxy population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".