MODELING ATMOSPHERIC EMISSION FOR CMB GROUND-BASED OBSERVATIONS
Bibliographic record
Abstract
Atmosphere is one of the most important noise sources for ground-based cosmic microwave background (CMB) experiments. By increasing optical loading on the detectors, it amplifies their effective noise, while its fluctuations introduce spatial and temporal correlations between detected signals. We present a physically motivated 3D-model of the atmosphere total intensity emission in the millimeter and sub-millimeter wavelengths. We derive a new analytical estimate for the correlation between detectors time-ordered data as a function of the instrument and survey design, as well as several atmospheric parameters such as wind, relative humidity, temperature and turbulence characteristics. Using an original numerical computation, we examine the effect of each physical parameter on the correlations in the time series of a given experiment. We then use a parametric-likelihood approach to validate the modeling and estimate atmosphere parameters from the polarbear-i project first season data set. We derive a new 1.0% upper limit on the linear polarization fraction of atmospheric emission. We also compare our results to previous studies and weather station measurements. The proposed model can be used for realistic simulations of future ground-based CMB observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".