Gravitational lensing analysis of the Kilo-Degree Survey
Bibliographic record
Abstract
The Kilo-Degree Survey (KiDS) is a multi-band imaging survey designed for cosmological studies from weak lensing and photometric redshifts. It uses the European Southern Observatory VLT Survey Telescope with its wide-field camera OmegaCAM. KiDS images are taken in four filters similar to the Sloan Digital Sky Survey ugri bands. The best seeing time is reserved for deep r-band observations. The median 5σ limiting AB magnitude is 24.9 and the median seeing is below 0.7 arcsec. Initial KiDS observations have concentrated on the Galaxy and Mass Assembly (GAMA) regions near the celestial equator, where extensive, highly complete redshift catalogues are available. A total of 109 survey tiles, 1 square degree each, form the basis of the first set of lensing analyses of halo properties of GAMA galaxies. Nine galaxies per square arcminute enter the lensing analysis, for an effective inverse shear variance of 69 arcmin−2. Accounting for the shape measurement weight, the median redshift of the sources is 0.53. KiDS data processing follows two parallel tracks, one optimized for weak lensing measurement and one for accurate matched-aperture photometry (for photometric redshifts). This technical paper describes the lensing and photometric redshift measurements (including a detailed description of the Gaussian aperture and photometry pipeline), summarizes the data quality and presents extensive tests for systematic errors that might affect the lensing analyses. We also provide first demonstrations of the suitability of the data for cosmological measurements, and describe our blinding procedure for preventing confirmation bias in the scientific analyses. The KiDS catalogues presented in this paper are released to the community through http://kids.strw.leidenuniv.nl.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.003 | 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".