COSMOS: STOCHASTIC BIAS FROM MEASUREMENTS OF WEAK LENSING AND GALAXY CLUSTERING
Bibliographic record
Abstract
In the theory of structure formation, galaxies are biased tracers of the underlying matter density field. The statistical relation between galaxy and matter density field is commonly referred to as galaxy bias. In this paper, we test the linear bias model with weak-lensing and galaxy clustering measurements in the 2 deg 2 COSMOS field. We estimate the bias of galaxies between redshifts z = 0.2 and z = 1 and over correlation scales between R = 0.2 h −1 Mpc and R = 15 h −1 Mpc. We focus on three galaxy samples, selected in flux (simultaneous cuts I 814 W < 26.5 and K s < 24) and in stellar mass (10 9 < M * < 10 10 h −2 M ☉ and 10 10 < M * < 10 11 h −2 M ☉ ). At scales R > 2 h −1 Mpc, our measurements support a model of bias increasing with redshift. The Tinker et al. fitting function provides a good fit to the data. We find the best-fit mass of the galaxy halos to be log ( M 200 / h −1 M ☉ ) = 11.7 +0.6 − 1.3 and log ( M 200 / h −1 M ☉ ) = 12.4 +0.2 − 2.9 , respectively, for the low and high stellar-mass samples. In the halo model framework, bias is scale dependent with a change of slope at the transition scale between the one and the two halo terms. We detect a scale dependence of bias with a turndown at scale R = 2.3 ± 1.5 h −1 Mpc, in agreement with previous galaxy clustering studies. We find no significant amount of stochasticity, suggesting that a linear bias model is sufficient to describe our data. We use N -body simulations to quantify both the amount of cosmic variance and systematic errors in the measurement.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".