The Massive and Distant Clusters of WISE Survey. X. Initial Results from a Sunyaev–Zeldovich Effect Study of Massive Galaxy Clusters at z > 1 Using MUSTANG2 on the GBT
Bibliographic record
Abstract
Abstract The properties of galaxy clusters as a function of redshift can be utilized as an important cosmological tool. We present initial results from a program of follow-up observations of the Sunyaev–Zeldovich effect (SZE) in high-redshift galaxy clusters detected by the Massive and Distant Clusters of WISE Survey (MaDCoWS) which uses infrared data from the Wide-field Infrared Survey (WISE) instrument. Using typical on-source integration times of 3–4 hr per cluster, MUSTANG2 on the Green Bank Telescope was able to measure strong detections of SZE decrements and statistically significant masses on 14 out of 16 targets. On the remaining two, weaker (3.7σ) detections of the SZE signal and strong upper limits on the masses were obtained. In this paper we present masses and pressure profiles of each target and outline the data analysis used to recover these quantities. Of the clusters with strong detections, three show significantly flatter pressure profiles while, from the MUSTANG2 data, five others show signs of disruption at their cores. However, outside of the cores of the clusters, we were unable to detect significant amounts of asymmetry. Finally, there are indications that the relationship between optical richness used by MaDCoWS and SZE-inferred mass may be significantly flatter than indicated in previous studies.
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 | 0.001 | 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) | 0.001 | 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".