The 1-1000 μm spectral energy distributions of far-infrared galaxies
Bibliographic record
Abstract
Galaxies selected at 170 μm by the Infrared Space Observatory (ISO) Far-IR BACKground (FIRBACK) survey represent the brightest ∼10 per cent of the cosmic infrared background. Examining their nature in detail is therefore crucial for constraining models of galaxy evolution. Here, we combine Spitzer archival data with previous near-infrared (near-IR), far-IR, and submillimetre (submm) observations of a representative sample of 22 FIRBACK galaxies spanning three orders of magnitude in IR luminosity. We fit a flexible, multicomponent, empirical SED model of star-forming galaxies designed to model the entire ∼1–1000 μm wavelength range. The fits are performed with a Markov Chain Monte Carlo (MCMC) approach, allowing for meaningful uncertainties to be derived. This approach also highlights degeneracies such as between Td and β, which we discuss in detail. From these fits and standard relations we derive: LIR, LPAH, star formation rate (SFR), τV, M*, Mdust, Td, and β. We look at a variety of correlations between these and combinations thereof in order to examine the physical nature of these galaxies. Our conclusions are supplemented by morphological examination of the sources, and comparison with local samples. We find the bulk of our sample to be consistent with fairly standard size and mass disc galaxies with somewhat enhanced star formation relative to local spirals, but likely not bona fide starbursts. A few higher-z luminous infrared galaxies (LIGs) and ultraluminous infrared galaxies (ULIGs) are also present, but contrary to expectation, they are weak mid-IR emitters and overall are consistent with star formation over an extended cold region rather than concentrated in the nuclear regions. We discuss the implications of this study for understanding populations detected at other wavelengths, such as the bright 850-μm Submillimetre Common-User Bolometer Array (SCUBA) sources or the faint Spitzer 24-μm sources.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".