Clustering of extremely red objects in the AKARI NEP-deep field
Bibliographic record
Abstract
Abstract We study the clustering property of extremely red objects (EROs) using Canada–France–Hawaii Telescope (CFHT) surveys with 0.55 deg2 in the AKARI north ecliptic pole (NEP) deep field. EROs are selected by the color criterion of r′ − Ks > 3.66, which is equivalent to (R − Ks)Vega > 5. We conducted the clustering analysis for two magnitude-limited cases, Ks < 20.3 (N = 363) and Ks < 20.9 (N = 727), using two-point angular correlation represented by a single power-law function. By fixing a power-law (with 0.8), the correlation lengths of EROs with Ks < 20.3 and Ks < 20.9 are 9.10 ± 1.86 and 7.81 ± 1.21 h−1 Mpc, respectively. We find that bias factors of EROs with Ks < 20.3 and Ks < 20.9 are 3.19 ± 0.59 and 2.83 ± 0.40, respectively, revealing that EROs reside in dark matter halos heavier than $\sim 10^{13}\, M_{\odot }$. To investigate possible descendants of EROs with Ks < 20.9, we calculate how the bias for dark matter halos that host EROs evolves by accounting for mass growth of halos along the redshift. We find that halos hosting EROs evolve into halos hosting local massive galaxies with 2–$7\, L^{*}$. It suggests that passive EROs with Ks < 20.9 are likely to be progenitors of massive galaxies in the present universe. The comparison between passive EROs (pEROs) and star-forming EROs (sEROs) classified by near-infrared colors shows that pEROs seem to be connected with more massive local galaxies. By fitting spectral energy distributions (SEDs), we estimate active galactic nucleus (AGN) contribution for 68 sEROs which are selected in mid-IR bands. AGN contributions to the IR luminosity are less than $10\%$ except for six sEROs. At least in the IR-selected sEROs, the contribution of AGN seems to be not significant.
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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 | 0.001 | 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) | 0.001 | 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".