350 μm Observations of Ultraluminous Infrared Galaxies at Intermediate Redshifts
Bibliographic record
Abstract
We present 350 μm observations of 36 ultraluminous infrared galaxies (ULIRGs) at intermediate redshifts (0.089 ≤ z ≤ 0.926) using the Submillimeter High Angular Resolution Camera II (SHARC-II) on the Caltech Submillimeter Observatory (CSO). In total, 28 sources are detected at S/N ≥ 3, providing the first flux measurements longward of 100 μm for a statistically significant sample of ULIRGs in the redshift range 0.1 ≲ z ≲ 1.0. Combining our 350 μm flux measurements with the existing IRAS 60 and 100 μm data, we fit a single-temperature model to the spectral energy distribution (SED) and thereby estimate dust temperatures and far-IR luminosities. Assuming an emissivity index of β = 1.5, we find a median dust temperature and far-IR luminosity of T d = 42.8 ± 7.1 K and log( L FIR / L ☉ ) = 12.2 ± 0.5, respectively. The far-IR-radio correlation observed in local star-forming galaxies is found to hold for ULIRGs in the redshift range 0.1 ≲ z ≲ 0.5, suggesting that the dust in these sources is predominantly heated by starbursts. We compare the far-IR luminosities and dust temperatures derived for dusty galaxy samples at low and high redshifts with our sample of ULIRGs at intermediate redshift. A general L FIR - T d relation is observed, albeit with significant scatter due to differing selection effects and variations in dust mass and grain properties. The relatively high dust temperatures observed for our sample compared to that of high- z submillimeter-selected starbursts with similar far-IR luminosities suggest that the dominant star formation in ULIRGs at moderate redshifts takes place on smaller spatial scales than is found at higher redshifts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".