SPECTROSCOPIC CONFIRMATION OF THREE RED-SEQUENCE SELECTED GALAXY CLUSTERS AT<i>z</i>= 0.87, 1.16, AND 1.21 FROM THE SPARCS SURVEY
Bibliographic record
Abstract
The Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS) is a z '-passband imaging survey of the 50 deg 2 Spitzer SWIRE Legacy fields, designed with the primary aim of creating the first large, homogeneously selected sample of massive clusters at z > 1. SpARCS uses an infrared adaptation of the two-filter cluster red-sequence technique. In this paper, we report Keck/LRIS spectroscopic confirmation of two new exceptionally rich galaxy clusters, SpARCS J161315+564930 at z = 0.871 ± 0.002, with 14 high-confidence members and a rest-frame velocity dispersion of σ v = 1230 ± 320 km s −1 , and SpARCS J161641+554513 at z = 1.161 ± 0.003, with seven high-confidence members (including one active galactic nucleus) and a rest-frame velocity dispersion of σ v = 950 ± 330 km s −1 . We also report confirmation of a third new system, SpARCS J161037+552417 at z = 1.210 ± 0.002, with seven high-confidence members and a rest-frame velocity dispersion of σ v = 410 ± 300 km s −1 . These three new spectroscopically confirmed clusters further demonstrate the efficiency and effectiveness of two-filter imaging for detecting bona fide galaxy clusters at high redshift. We conclude by demonstrating that prospects are good for the current generation of surveys aiming to estimate cluster redshifts and masses at z ≳ 1 directly from optical-infrared imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".