MétaCan
Menu
Back to cohort
Record W2034161363 · doi:10.1093/mnrasl/sls040

<scp>music</scp> for Faraday rotation measure synthesis

2012· article· en· W2034161363 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeconvolutionFaraday effectFaraday cageMeasure (data warehouse)AlgorithmPhysicsFunction (biology)MathematicsComputer scienceQuantum mechanicsData mining

Abstract

fetched live from OpenAlex

Abstract Faraday rotation measure (RM) synthesis requires the recovery of the Faraday dispersion function (FDF) from measurements restricted to limited wavelength ranges, which is an ill-conditioned deconvolution problem. Here, we propose a novel deconvolution method based on an extension of the MUltiple SIgnal Classification (music) algorithm. The complexity and speed of the method is determined by the eigen-decomposition of the covariance matrix of the observed polarizations. We show numerically that for high to moderate signal-to-noise ratio (S/N) cases the rm-music method is able to recover the Faraday depth values of closely spaced pairs of thin RM components, even in situations where the peak response of the FDF is outside of the RM range between the two input RM components. This result is particularly important because the standard deconvolution approach based on rm-clean fails systematically in such situations, due to its greedy mechanism used to extract the RM components. For low S/N situations, both the rm-music and rm-clean methods provide similar results.

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.

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 score0.045
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.179
Teacher spread0.167 · 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