An Assessment of Contamination in the Thermal-SZ Map Using Cross-correlations
Bibliographic record
Abstract
Abstract We search for potential galactic and extragalactic dust contamination in thermal Sunyaev–Zeldovich maps derived from the Planck data. To test for contamination, we apply a variety of galactic dust and cosmic infrared background (CIB) models to the data as part of the y map reconstruction process. We evaluate the level of contamination by cross-correlating these y maps with mass tracers based on weak lensing data. The lensing data we use are the convergence map, κ, from the Red Sequence Cluster Lensing survey, and the cosmic microwave background (CMB) lensing potential map, ϕ, from the Planck Collaboration. We make a CIB-subtracted y map and measure the cross-correlation between it and the lensing data. By comparing it with CIB-contaminated cross-correlation, we find that the cross-correlation between κ and y is only slightly contaminated by CIB signal, at the level of 6.8 ± 3.5%, which implies that previous detections of κ × y are robust to CIB contamination. However, we find that ϕ × y is more significantly contaminated, by 16.7 ± 3.5%, because the CMB lensing potential probes higher redshift sources that overlap more with the CIB sources. We find that Galactic dust does not significantly contaminate either cross-correlation signal.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".