Characterizing line-of-sight variability of polarized dust emission with future CMB experiments
Bibliographic record
Abstract
ABSTRACT While Galactic dust emission is often accounted for in cosmic microwave background (CMB) analyses by fitting a two-parameter modified blackbody (MBB) model in each pixel, typically a number of such clouds are found along each line of sight and within each angular pixel, resulting in a superposition of their spectra. We study the effects of this superposition on pixel-based foreground fitting strategies by modeling the spectral energy distribution (SED) in each pixel as the integral of individual MBB spectra over various physically motivated statistical distributions of dust cloud properties. We show that fitting these SEDs with the two-parameter MBB model generally results in unbiased estimates of the CMB Stokes Q and U amplitudes per pixel, unless there are significant changes in both the dust SED and polarization angle along the line of sight, in which case significant (>10σ) biases are observed in an illustrative model. We find that the best-fitting values of the dust temperature, Td, and spectral index, βd, are significantly biased from the mean/median of the corresponding statistical distributions when the distributions are broad, suggesting that MBB model fits can give an unrepresentative picture of the physical properties of dust at microwave wavelengths if not interpreted carefully. Using Fisher matrix analysis, we determine the experimental sensitivity required to recover the parameters of the Td and βd distributions by fitting a probabilistic MBB model, finding that only the parameters of broad distributions can be measured by SED fitting on a single line of sight.
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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".