LINKING GALAXIES TO DARK MATTER HALOS AT<i>z</i>∼ 1: DEPENDENCE OF GALAXY CLUSTERING ON STELLAR MASS AND SPECIFIC STAR FORMATION RATE
Bibliographic record
Abstract
We study the dependence of angular two-point correlation functions on stellar mass ( M * ) and specific star formation rate (sSFR) of galaxies at . The data from the UK Infrared Telescope Infrared Deep Sky Survey Deep eXtragalactic Survey and Canada–France–Hawaii Telescope Legacy Survey cover 8.2 deg 2 sample scales larger than 100 at , allowing us to investigate the correlation between clustering, M * , and star formation through halo modeling. Based on halo occupation distributions (HODs) of M * threshold samples, we derive HODs for M * binned galaxies, and then calculate the ratio. The ratio for central galaxies shows a peak at , and satellites predominantly contribute to the total stellar mass in cluster environments with values of 0.01–0.02. Using star-forming galaxies split by sSFR, we find that main sequence galaxies ( ) are mainly central galaxies in halos with the lowest clustering amplitude, while lower sSFR galaxies consist of a mixture of both central and satellite galaxies where those with the lowest M * are predominantly satellites influenced by their environment. Considering the lowest samples in each M * bin, massive central galaxies reside in more massive halos with lower sSFRs than low mass ones, indicating star-forming central galaxies evolve from a low M * –high sSFR to a high M * –low sSFR regime. We also find that the most rapidly star-forming galaxies ( ) are in more massive halos than main sequence ones, possibly implying galaxy mergers in dense environments are driving the active star formation. These results support the conclusion that the majority of star-forming galaxies follow secular evolution through the sustained but decreasing formation of stars.
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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 | 0.003 |
| 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 | 0.001 | 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".