SPECTROSCOPIC CONFIRMATION OF A MASSIVE RED-SEQUENCE-SELECTED GALAXY CLUSTER AT<i>z</i>= 1.34 IN THE SpARCS-SOUTH CLUSTER SURVEY
Bibliographic record
Abstract
The Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS) is a z '-passband imaging survey, consisting of deep ( z ' ≃ 24 AB) observations made from both hemispheres using the CFHT 3.6 m and CTIO 4 m telescopes. The survey was designed with the primary aim of detecting galaxy clusters at z > 1. In tandem with pre-existing 3.6 μm observations from the Spitzer Space Telescope SWIRE Legacy Survey, SpARCS detects clusters using an infrared adaptation of the two-filter red-sequence cluster technique. The total effective area of the SpARCS cluster survey is 41.9 deg 2 . In this paper, we provide an overview of the 13.6 deg 2 Southern CTIO/MOSAIC II observations. The 28.3 deg 2 Northern CFHT/MegaCam observations are summarized in a companion paper by Muzzin et al. In this paper, we also report spectroscopic confirmation of SpARCS J003550−431224, a very rich galaxy cluster at z = 1.335, discovered in the ELAIS-S1 field. To date, this is the highest spectroscopically confirmed redshift for a galaxy cluster discovered using the red-sequence technique. Based on nine confirmed members, SpARCS J003550−431224 has a preliminary velocity dispersion of 1050 ± 230 km s −1 . With its proven capability for efficient cluster detection, SpARCS is a demonstration that we have entered an era of large, homogeneously selected z > 1 cluster surveys.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".