Measures of star formation rates from infrared (<i>Herschel</i>) and UV (<i>GALEX</i>) emissions of galaxies in the HerMES fields
Bibliographic record
Abstract
ABSTRACT The reliability of infrared (IR) and ultraviolet (UV) emissions to measure star formation rates (SFRs) in galaxies is investigated for a large sample of galaxies observed with the Spectral and Photometric Imaging Receiver (SPIRE) and the Photodetector Array Camera and Spectrometer (PACS) instruments on Herschel as part of the Herschel Multi-Tiered Extragalactic Survey (HerMES) project. We build flux-limited 250-μm samples of sources at redshift z < 1, cross-matched with the Spitzer/MIPS and GALEX catalogues. About 60 per cent of the Herschel sources are detected in UV. The total IR luminosities, LIR, of the sources are estimated using a spectral energy distribution (SED) fitting code that fits to fluxes between 24 and 500 μm. Dust attenuation is discussed on the basis of commonly used diagnostics: the LIR/LUV ratio and the slope, β, of the UV continuum. A mean dust attenuation AUV of mag is measured in the samples. LIR/LUV is found to correlate with LIR. Galaxies with and 0.5 < z < 1 exhibit a mean dust attenuation AUV of about 0.7 mag lower than that found for their local counterparts, although with a large dispersion. Our galaxy samples span a large range of β and LIR/LUV values which, for the most part, are distributed between the ranges defined by the relations found locally for starburst and normal star-forming galaxies. As a consequence the recipe commonly applied to local starbursts is found to overestimate the dust attenuation correction in our galaxy sample by a factor of ∼2–3. The SFRs deduced from LIR are found to account for about 90 per cent of the total SFR; this percentage drops to 71 per cent for galaxies with (or ). For these faint objects, one needs to combine UV and IR emissions to obtain an accurate measure of the SFR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".