Evolution of mid-infrared galaxy luminosity functions from the entire <i>AKARI</i> NEP deep field with new CFHT photometry
Bibliographic record
Abstract
We present infrared (IR) galaxy luminosity functions (LFs) in the <i>AKARI</i> North Ecliptic Pole (NEP) deep field using recently-obtained, wider Canada–France–Hawaii Telescope (CFHT) optical/near-IR images. <i>AKARI</i> has obtained deep images in the mid-infrared (IR), covering 0.6 deg<sup>2</sup> of the NEP deep field. However, our previous work was limited to the central area of 0.25 deg<sup>2</sup> due to the lack of optical coverage of the full <i>AKARI</i> NEP survey. To rectify the situation, we recently obtained CFHT optical and near-IR images over the entire <i>AKARI</i> NEP deep field. These new CFHT images are used to derive accurate photometric redshifts, allowing us to fully exploit the whole <i>AKARI</i> NEP deep field. <i>AKARI</i>'s deep, continuous filter coverage in the mid-IR wavelengths (2.4, 3.2, 4.1, 7, 9, 11, 15, 18, and 24 μm) exists nowhere else, due to filter gaps of other space telescopes. It allows us to estimate rest-frame 8 and 12 μm luminosities without using a large extrapolation based on spectral energy distribution fitting, which was the largest uncertainty in previous studies. Total infrared (TIR) luminosity is also obtained more reliably due to the superior filter coverage. The resulting rest-frame 8 and 12 μm, and TIR LFs at 0.15 < z < 2.2 are consistent with previous works, but with reduced uncertainties, especially at the high-luminosity end, thanks to the wide-field coverage. In terms of cosmic infrared luminosity density (ΩIR), we found that the ΩIR evolves as ∝ (1 + z)<sup>4.2 ± 0.4</sup>.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 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 teacher head, 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".