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Record W4409788573 · doi:10.1093/mnras/staf668

Bayesian evidence for flux scale errors in Galactic synchrotron maps

2025· article· en· W4409788573 on OpenAlexafffund
Michael J. Wilensky, Melis O Irfan, Philip Bull

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsMcGill University
FundersH2020 European Research CouncilEuropean CommissionScience and Technology Facilities CouncilNational Research FoundationCanadian Institute for Theoretical AstrophysicsArthritis National Research Foundation
KeywordsPhysicsAstrophysicsScale (ratio)Flux (metallurgy)AstronomyBayesian probabilitySynchrotronStatistical physicsNuclear physicsStatistics

Abstract

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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.247
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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