SPECTROSCOPIC CONFIRMATION OF TWO MASSIVE RED-SEQUENCE-SELECTED GALAXY CLUSTERS AT<i>z</i>∼ 1.2 IN THE SpARCS-NORTH CLUSTER SURVEY
Bibliographic record
Abstract
The Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS) is a deep z '-band imaging survey covering the Spitzer Wide-Area Infrared Extragalactic Survey (SWIRE) Legacy fields designed to create the first large homogeneously selected sample of massive clusters at z > 1 using an infrared adaptation of the cluster red-sequence method. We present an overview of the northern component of the survey which has been observed with Canada–France–Hawaii Telescope (CFHT)/MegaCam and covers 28.3 deg 2 . The southern component of the survey was observed with Cerro Tololo Inter-American Observatory (CTIO)/MOSAICII, covers 13.6 deg 2 , and is summarized in a companion paper by Wilson et al. We also present spectroscopic confirmation of two rich cluster candidates at z ∼ 1.2. Based on Nod-and-Shuffle spectroscopy from GMOS-N on Gemini, there are 17 and 28 confirmed cluster members in SpARCS J163435+402151 and SpARCS J163852+403843 which have spectroscopic redshifts of 1.1798 and 1.1963, respectively. The clusters have velocity dispersions of 490 ± 140 km s −1 and 650 ± 160 km s −1 , respectively, which imply masses ( M 200 ) of (1.0 ± 0.9) × 10 14 M ☉ and (2.4 ± 1.8) × 10 14 M ☉ . Confirmation of these candidates as bonafide massive clusters demonstrates that two-filter imaging is an effective, yet observationally efficient, method for selecting clusters at z > 1.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".