Environmental dependence of 8 μm luminosity functions of galaxies at z ~ 0.8: Comparison between RXJ1716.4+6708 and the AKARI NEP-deep field
Bibliographic record
Abstract
Aims. We aim to reveal environmental dependence of infrared luminosity functions (IR LFs) of galaxies at z ~ 0.8 using the AKARI \nsatellite. AKARI’s wide field of view and unique mid-IR filters help us to construct restframe 8 μm LFs directly without relying on \nSED models. \nMethods. We construct restframe 8 μm IR LFs in the cluster region RXJ1716.4+6708 at z = 0.81, and compare them with a blank \nfield using the AKARI north ecliptic pole deep field data at the same redshift. AKARI’s wide field of view (10' × 10') is suitable to \ninvestigate wide range of galaxy environments. AKARI’s 15 μm filter is advantageous here since it directly probes restframe 8 μm at \nz ~ 0.8, without relying on a large extrapolation based on a SED fit, which was the largest uncertainty in previous work. \nResults. We have found that cluster IR LFs at restframe 8 μm have a factor of 2.4 smaller L^∗ and a steeper faint-end slope than that \nof the field. Confirming this trend, we also found that faint-end slopes of the cluster LFs becomes flatter and flatter with decreasing \nlocal galaxy density. These changes in LFs cannot be explained by a simple infall of field galaxy population into a cluster. Physics \nthat can preferentially suppress IR luminous galaxies in high density regions is required to explain the observed results.
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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 | 0.001 |
| 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".