Millimeter Observation of the SZ Effect in the Corona Borealis Supercluster
Bibliographic record
Abstract
We have observed the Corona Borealis supercluster with the Millimeter and Infrared Testa Grigia Observatory (MITO), located in the Italian Alps, at 143, 214, 272, and 353 GHz. We present a description of the measurements, data analysis, and results of the observations together with a comparison with observations performed at 33 GHz with the Very Small Array (VSA) interferometer situated at the Teide Observatory (Tenerife, Spain). Observations have been made in the direction of the supercluster toward a cosmic microwave background (CMB) cold spot previously detected in a VSA temperature map. Observational strategy and data analysis are described in detail, explaining the procedures used to disentangle primary and secondary anisotropies in the resulting maps. From a first level of data analysis, we find evidence in MITO data of primary anisotropy but still with room for the presence of secondary anisotropy, especially when VSA results are included. With a second level of data analysis using map making and the maximum entropy method, we claim a weak detection of a faint signal compatible with a SZ effect, characterized at most by a Comptonization parameter y = (7.8 ) × 10 -6 68% CL. The low level of confidence in the presence of a SZ signal invites us to study this sky region with higher sensitivity and angular resolution experiments such as the already-planned upgraded versions of VSA and MITO.
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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 | 0.000 | 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) | 0.000 | 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 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".