THE<i>HERSCHEL</i>COMPREHENSIVE (U)LIRG EMISSION SURVEY (HERCULES): CO LADDERS, FINE STRUCTURE LINES, AND NEUTRAL GAS COOLING
Bibliographic record
Abstract
(Ultra) luminous infrared galaxies ((U)LIRGs) are objects characterized by their extreme infrared (8–1000 μm) luminosities ( L LIRG > 10 11 L ☉ and L ULIRG > 10 12 L ☉ ). The Herschel Comprehensive ULIRG Emission Survey (PI: van der Werf) presents a representative flux-limited sample of 29 (U)LIRGs that spans the full luminosity range of these objects (10 11 L ☉ ⩽ L IR ⩽ 10 13 L ☉ ). With the Herschel Space Observatory , we observe [C ii ] 157 μm, [O i ] 63 μm, and [O i ] 145 μm line emission with Photodetector Array Camera and Spectrometer, CO J = 4–3 through J = 13–12, [C i ] 370 μm, and [C i ] 609 μm with SPIRE, and low- J CO transitions with ground-based telescopes. The CO ladders of the sample are separated into three classes based on their excitation level. In 13 of the galaxies, the [O i ] 63 μm emission line is self absorbed. Comparing the CO excitation to the InfraRed Astronomical Satellite 60/100 μm ratio and to far infrared luminosity, we find that the CO excitation is more correlated to the far infrared colors. We present cooling budgets for the galaxies and find fine-structure line flux deficits in the [C ii ], [Si ii ], [O i ], and [C i ] lines in the objects with the highest far IR fluxes, but do not observe this for CO 4 ⩽ J upp ⩽ 13. In order to study the heating of the molecular gas, we present a combination of three diagnostic quantities to help determine the dominant heating source. Using the CO excitation, the CO J = 1–0 linewidth, and the active galactic nucleus (AGN) contribution, we conclude that galaxies with large CO linewidths always have high-excitation CO ladders, and often low AGN contributions, suggesting that mechanical heating is important.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".