CLUSTERING PROPERTIES OF B<i>z</i>K-SELECTED GALAXIES IN GOODS-N: ENVIRONMENTAL QUENCHING AND TRIGGERING OF STAR FORMATION AT<i>z</i>∼ 2
Bibliographic record
Abstract
Using a sample of B z K-selected galaxies at z ∼ 2 identified from the CFHT/WIRCAM near-infrared survey of GOODS-North, we discuss the relation between star formation rate (SFR), specific star formation rate (SSFR), and stellar mass ( M * ), and the clustering of galaxies as a function of these parameters. For star-forming galaxies ( sBzK s), the UV-based SFR, corrected for extinction, scales with the stellar mass as SFR∝ M α * with α = 0.74 ± 0.20 down to M * ∼ 10 9 M ☉ , indicating a weak dependence on the stellar mass of the SFR efficiency, namely, SSFR. We also measure the angular correlation function and hence infer the correlation length for sBzK galaxies as a function of M * , SFR, and SSFR, as well as K -band apparent magnitude. We show that passive galaxies ( pBzK s) are more strongly clustered than sBzK galaxies at a given stellar mass, mirroring the color–density relation seen at lower redshifts. We also find that the correlation length of sBzK galaxies ranges from 4 to 20 h −1 Mpc, being a strong function of M K , M * , and SFR. On the other hand, the clustering dependence on SSFR changes abruptly at 2 × 10 −9 yr −1 , which is the typical value for "main-sequence" star-forming galaxies at z ∼ 2. We show that the correlation length reaches a minimum at this characteristic value, and is larger for galaxies with both smaller and larger SSFRs; a dichotomy that is only marginally implied from the predictions of the semi-analytical models. Our results suggest that there are two types of environmental effects at work at z ∼ 2. Stronger clustering for relatively quiescent galaxies implies that the environment has started to play a role in quenching star formation. At the same time, stronger clustering for galaxies with elevated SSFRs ("starbursts") might be attributed to an increased efficiency for galaxy interactions and mergers in dense environments.
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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 | 0.000 |
| 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".