Comparison between the Blue and Red Galaxy Alignments Detected in the Sloan Digital Sky Survey
Bibliographic record
Abstract
We measure the intrinsic alignments of the blue and the red galaxies separately by analyzing the spectroscopic data of the Sloan Digital Sky Survey Data Release 6 (SDSS DR6). For both samples of the red and blue galaxies with axial ratios of b / a ≤ 0.8, we detect a 3 σ signal of the ellipticity correlation in the redshift range of 0 ≤ z ≤ 0.4 for an r -band absolute (model) magnitude cut of M r ≤ -19.2 (no K -correction). We note a difference in the strength and the distance scale for the red and the blue galaxy correlation η 2D ( r ): For the bright blue galaxies, it behaves as a quadratic scaling of the linear density correlation of ξ( r ) as η 2D ( r ) ∝ ξ 2 ( r ) with strong signal detected only at a small distance bin of r ≤ 3 h -1 Mpc, while for the bright red galaxies it follows a linear scaling as η 2D ( r ) ∝ ξ( r ) with signals detected at larger distance out to r ~ 6 h -1 Mpc. We also test whether the detected correlation signal is intrinsic or spurious by quantifying the systematic error and find that the effect of the systematic error on the ellipticity correlation is negligible. It is finally concluded that our results will be useful for the weak lensing measurements as well as the understanding of the large-scale structure formation.
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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 | 0.001 | 0.002 |
| 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".