The 2-degree Field Lensing Survey: design and clustering measurements
Bibliographic record
Abstract
We present the 2-degree Field Lensing Survey (2dFLenS), a new galaxy redshift survey performed at the Anglo-Australian Telescope. 2dFLenS is the first wide-area spectroscopic survey specifically targeting the area mapped by deep-imaging gravitational lensing fields, in this case the Kilo-Degree Survey. 2dFLenS obtained 70 079 redshifts in the range z < 0.9 over an area of 731 deg2, and is designed to extend the data sets available for testing gravitational physics and promote the development of relevant algorithms for joint imaging and spectroscopic analysis. The redshift sample consists first of 40 531 Luminous Red Galaxies (LRGs), which enable analyses of galaxy-galaxy lensing, redshift-space distortion, and the overlapping source redshift distribution by cross-correlation. An additional 28 269 redshifts form a magnitude-limited (r < 19.5) nearly complete subsample, allowing direct source classification and photometric-redshift calibration. In this paper, we describe the motivation, target selection, spectroscopic observations, and clustering analysis of 2dFLenS. We use power spectrum multipole measurements to fit the redshift-space distortion parameter of the LRG sample in two redshift ranges 0.15 < z < 0.43 and 0.43 < z < 0.7 as β = 0.49 ± 0.15 and β = 0.26 ± 0.09, respectively. These values are consistent with those obtained from LRGs in the Baryon Oscillation Spectroscopic Survey. 2dFLenS data products will be released via our website http://2dflens.swin.edu.au.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".