Far-infrared luminosity function of local star-forming galaxies in the AKARI Deep Field-South
Bibliographic record
Abstract
We present a far-infrared galaxy luminosity function for the local universe. We have obtained 389 spectroscopic redshifts for galaxies observed at 90 μm in the AKARI Deep Field-South, using the AAOmega fibre spectrograph via optical identifications in the digitized sky survey and 4-m class optical imaging. For the luminosity function presented in this paper, we have used those galaxies which have redshifts 0 < z < 0.25, have optical magnitudes and are not part of a newly discovered cluster of galaxies (giving a total of 130 sources). Infrared and optical completeness functions were estimated using earlier Spitzer data and automated plate measurement (APM) B-band optical data, respectively, and the luminosity function has been prepared using the 1/Vmax method. We also separate the luminosity function between galaxies which show evidence of predominantly star-forming activity and predominantly active galactic nucleus (AGN) activity in their optical spectra. Our luminosity function is in good agreement with the previous 90-μm luminosity function from the European Large Area Infrared Space Observatory (ISO) Survey, and we also present a luminosity function with combined AKARI and ISO data. The result is in reasonable agreement with predictions based on the earlier Infrared Astronomical Satellite (IRAS) m PSCz catalogue, and with a recent backward evolution model.
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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 | 0.002 | 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) | 0.002 | 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".