<i>Herschel</i>unveils a puzzling uniformity of distant dusty galaxies
Bibliographic record
Abstract
The <i>Herschel<i/> Space Observatory enables us to accurately measure the bolometric output of starburst galaxies and active galactic nuclei (AGN) by directly sampling the peak of their far-infrared (IR) emission. Here we examine whether the spectral energy distribution (SED) and dust temperature of galaxies have strongly evolved over the last 80% of the age of the Universe. We discuss possible consequences for the determination of star-formation rates (SFR) and any evidence for a major change in their star-formation properties. We use <i>Herschel<i/> deep extragalactic surveys from 100 to 500 <i>μ<i/>m to compute total IR luminosities in galaxies down to the faintest levels, using PACS and SPIRE in the GOODS-North field (PEP and HerMES key programs). An extension to fainter luminosities is done by stacking images on 24 <i>μ<i/>m prior positions. We show that measurements in the SPIRE bands can be used below the <i>statistical<i/> confusion limit if information at higher spatial resolution is used, e.g. at 24 <i>μ<i/>m, to identify “isolated” galaxies whose flux is not boosted by bright neighbors. Below <i>z<i/> ~ 1.5, mid-IR extrapolations are correct for star-forming galaxies with a dispersion of only 40% (0.15 dex), therefore similar to <i>z<i/> ~ 0 galaxies, over three decades in luminosity below the regime of ultra-luminous IR galaxies (ULIRGs, <i>L<i/><sub>IR<sub/> <i>≥<i/> 10<sup>12<sup/> ). This narrow distribution is puzzling when considering the range of physical processes that could have affected the SED of these galaxies. Extrapolations from only one of the 160 <i>μ<i/>m, 250 <i>μ<i/>m or 350 <i>μ<i/>m bands alone tend to overestimate the total IR luminosity. This may be explained by the lack of far-IR constraints around and above ~150 <i>μ<i/>m (rest-frame) before <i>Herschel<i/> on those templates. We also note that the dust temperature of luminous IR galaxies (LIRGs, <i>L<i/><sub>IR<sub/> <i>≥<i/> 10<sup>11<sup/> ) around <i>z<i/> ~ 1 is mildly colder by 10–15% than their local analogs and up to 20% for ULIRGs at <i>z<i/> ~ 1.6 (using a single modified blackbody-fit to the peak far-IR emission with an emissivity index of <i>β<i/> = 1.5). Above <i>z<i/> = 1.5, distant galaxies are found to exhibit a substantially larger mid- over far-IR ratio, which could either result from stronger broad emission lines or warm dust continuum heated by a hidden AGN. Two thirds of the AGNs identified in the field with a measured redshift exhibit the same behavior as purely star-forming galaxies. Hence a large fraction of AGNs harbor coeval star formation at very high SFR and in conditions similar to purely star-forming galaxies.
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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".