The HectoMAP Redshift Survey: First Data Release
Bibliographic record
Abstract
Abstract HectoMAP is a dense, red-selected redshift survey to a limiting r = 21.3 mag covering 55 deg2 in a contiguous 1.°5 strip across the northern sky. This region is also covered by the Subaru/Hyper Suprime-Cam (HSC) Subaru Strategic Program photometric survey enabling a range of applications that combine a dense foreground redshift survey with both strong and weak lensing maps. The median redshift of HectoMAP exceeds 0.3 throughout the survey region, and the mean density of the redshift survey is ∼2000 galaxies deg−2. Here we report a total of 17,313 redshifts in a first data release covering 8.7 deg2. We include the derived quantities D n 4000 and stellar mass for nearly all of the objects. Among these galaxies, 8117 constitute a 79% complete red-selected subsample with r ≤ 20.5 mag, and an additional 4318 constitute a 68% complete red-selected subsample with 20.5 < r(mag) < 21.3. As examples of the strengths of HectoMAP data, we discuss two applications: refined membership of redMaPPer photometrically selected clusters and a test of HSC photometric redshifts. We highlight a remarkable redMaPPer strong lensing system. The comparison of photometric redshifts with spectroscopic redshifts in a dense survey uncovers subtle systematic issues in the photometric redshifts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.020 |
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".