Microwave Spectro-Polarimetry of Matter and Radiation across Space and\n Time
Bibliographic record
Abstract
This paper discusses the science case for a sensitive spectro-polarimetric\nsurvey of the microwave sky. Such a survey would provide a tomographic and\ndynamic census of the three-dimensional distribution of hot gas, velocity\nflows, early metals, dust, and mass distribution in the entire Hubble volume,\nexploit CMB temperature and polarisation anisotropies down to fundamental\nlimits, and track energy injection and absorption into the radiation background\nacross cosmic times by measuring spectral distortions of the CMB blackbody\nemission. In addition to its exceptional capability for cosmology and\nfundamental physics, such a survey would provide an unprecedented view of\nmicrowave emissions at sub-arcminute to few-arcminute angular resolution in\nhundreds of frequency channels, a data set that would be of immense legacy\nvalue for many branches of astrophysics. We propose that this survey be\ncarried-out with a large space mission featuring a broad-band polarised imager\nand a moderate resolution spectro-imager at the focus of a 3.5m aperture\ntelescope actively cooled to about 8K, complemented with absolutely-calibrated\nFourier Transform Spectrometer modules observing at degree-scale angular\nresolution in the 10-2000 GHz frequency range. We propose two observing modes:\na survey mode to map the entire sky as well as a few selected wide fields, and\nan observatory mode for deeper observations of regions of specific interest.\n
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".