Evolution of infrared luminosity functions of galaxies in the AKARI NEP-deep field
Bibliographic record
Abstract
Aims. Dust-obscured star-formation increases with increasing intensity and increasing redshift. We aim to reveal the cosmic star-formation history obscured by dust using deep infrared observation with AKARI.Methods. We constructed restframe 8 μm, 12 μm, and total infrared (TIR) luminosity functions (LFs) at 0.15 < z < 2.2 using 4128 infrared sources in the AKARI NEP-deep field. A continuous filter coverage in the mid-IR wavelength (2.4, 3.2, 4.1, 7, 9, 11, 15, 18, and 24 μm) by the AKARI satellite allowed us to estimate restframe 8 μm and 12 μm luminosities without using a large extrapolation based on an SED fit, which was the largest uncertainty in previous work.Results. We find that all 8 μm (0.38 < z < 2.2), 12 μm (0.15 < z < 1.16), and TIR LFs (0.2 < z <1.6) show continuous and strong evolution toward higher redshift. Our direct estimate of 8 μm LFs is useful since previous work often had to use a large extrapolation from the Spitzer 24 μm to 8 μm, where SED modeling is more difficult because of the PAH emissions. In terms of cosmic infrared luminosity density (ΩIR), which was obtained by integrating analytic fits to the LFs, we find good agreement with previous work at z<1.2. We find the ΩIR evolves as (1 + z). When we separate contributions to ΩIR by LIRGs and ULIRGs, we found more IR luminous sources are increasingly more important at higher redshift. We find that the ULIRG (LIRG) contribution increases by a factor of 10 (1.8) from z = 0.35 to z = 1.4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".