Sensitive 3 mm Imaging of Discrete Sources in the Fields of Thermal Sunyaev–Zel’dovich Effect–Selected Galaxy Clusters
Bibliographic record
Abstract
Abstract In this paper, we present the results of a blind survey for compact sources in 243 Galaxy clusters that were identified using the thermal Sunyaev–Zel'dovich effect (tSZ). The survey was carried out at 90 GHz using MUSTANG2 on the Green Bank Telescope and achieved a 5σ detection limit of 1 mJy in the center of each cluster. We detected 24 discrete sources. The majority (18) of these correspond to known radio sources, and of these, five show signs of significant variability, either with time or in spectral index. The remaining sources have no clear counterparts at other wavelengths. Searches for galaxy clusters via the tSZ strongly rely on observations at 90 GHz, and the sources found have the potential to bias mass estimates of clusters. We compare our results to the Websky simulation that can be used to estimate the source contamination in galaxy cluster catalogs. While the simulation shows a good match to our observations at the clusters’ centers, it does not match our source distribution further out. Sources over 104″ from a cluster’s center bias the tSZ signal high, for some of the sources found, by over 50%. When averaged over the whole cluster population, the effect is smaller but still at a level of 1%–2%. We also discovered that unlike previous measurements and simulations, we see an enhancement of source counts in the outer regions of the clusters and fewer sources than expected in the centers of this tSZ-selected sample.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".