VICS82: The VISTA–CFHT Stripe 82 Near-infrared Survey
Bibliographic record
Abstract
Abstract We present the VISTA–CFHT Stripe 82 (VICS82) survey: a near-infrared (J+K s ) survey covering 150 square degrees of the Sloan Digital Sky Survey (SDSS) equatorial Stripe 82 to an average depth of J = 21.9 AB mag and K s = 21.4 AB mag (80% completeness limits; 5σ point-source depths are approximately 0.5 mag brighter). VICS82 contributes to the growing legacy of multiwavelength data in the Stripe 82 footprint. The addition of near-infrared photometry to the existing SDSS Stripe 82 coadd ugriz photometry reduces the scatter in stellar mass estimates to dex for galaxies with at , and offers improvement compared to optical-only estimates out to , with stellar masses constrained within a factor of approximately 2.5. When combined with other multiwavelength imaging of the Stripe, including moderate-to-deep ultraviolet (GALEX), optical and mid-infrared (Spitzer-IRAC) coverage, as well as tens of thousands of spectroscopic redshifts, VICS82 gives access to approximately 0.5 Gpc3 of comoving volume. Some of the main science drivers of VICS82 include (a) measuring the stellar mass function of galaxies out to ; (b) detecting intermediate-redshift quasars at (c) measuring the stellar mass function and baryon census of clusters of galaxies, and (d) performing cross-correlation experiments of cosmic microwave background lensing in the optical/near-infrared that link stellar mass to large-scale dark matter structure. Here we define and describe the survey, highlight some early science results, and present the first public data release, which includes an SDSS-matched catalog as well as the calibrated pixel data themselves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".