Bayesian evidence for flux scale errors in Galactic synchrotron maps
Bibliographic record
Abstract
ABSTRACT The 408 MHz Haslam map is widely used as a low-frequency anchor for the intensity and morphology of Galactic synchrotron emission. Multifrequency, multi-experiment fits show evidence of spatial variation and curvature in the synchrotron frequency spectrum, but there are also poorly understood multiplicative flux scale disagreements between experiments. We perform a Bayesian model comparison across a range of scenarios, using fits that include recent spectroscopic observations at $\sim 1$ GHz by MeerKAT as well as a reference map from the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA) at 73 MHz. In the few square degrees that we analysed, a large uncorrected flux scale factor potentially as large as 1.6 in the Haslam data is preferred, indicating a 60 per cent overestimation of the brightness. This partly undermines its use as a reference map. We also find that models with non-zero spectral curvature are statistically disfavoured. Given the limited sky coverage here, we suggest a similar analysis across many more regions of the sky to determine the extent and variation of flux scale errors, and whether they should be treated as random or systematic errors in analyses that use the Haslam map as a template.
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 | 0.017 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".