Detection of spectral variations of Anomalous Microwave Emission with QUIJOTE and C-BASS
Bibliographic record
Abstract
ABSTRACT Anomalous Microwave Emission (AME) is a significant component of Galactic diffuse emission in the frequency range 10–$60\, \mathrm{GHz}$ and a new window into the properties of sub-nanometre-sized grains in the interstellar medium. We investigate the morphology of AME in the ≈10○ diameter λ Orionis ring by combining intensity data from the QUIJOTE experiment at 11, 13, 17, and $19\, \mathrm{GHz}$ and the C-Band All Sky Survey (C-BASS) at $4.76\, \mathrm{GHz}$, together with 19 ancillary data sets between 1.42 and $3000\, \mathrm{GHz}$. Maps of physical parameters at 1○ resolution are produced through Markov chain Monte Carlo (MCMC) fits of spectral energy distributions (SEDs), approximating the AME component with a lognormal distribution. AME is detected in excess of $20\, \sigma$ at degree-scales around the entirety of the ring along photodissociation regions (PDRs), with three primary bright regions containing dark clouds. A radial decrease is observed in the AME peak frequency from $\approx 35\, \mathrm{GHz}$ near the free–free region to $\approx 21\, \mathrm{GHz}$ in the outer regions of the ring, which is the first detection of AME spectral variations across a single region. A strong correlation between AME peak frequency, emission measure and dust temperature is an indication for the dependence of the AME peak frequency on the local radiation field. The AME amplitude normalized by the optical depth is also strongly correlated with the radiation field, giving an overall picture consistent with spinning dust where the local radiation field plays a key role.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".