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Record W3094912910 · doi:10.1093/mnras/staa3353

The θ–θ diagram: transforming pulsar scintillation spectra to coordinates on highly anisotropic interstellar scattering screens

2020· article· en· W3094912910 on OpenAlexaff
Tim Sprenger, O. Wucknitz, Robert Main, Daniel Baker, Walter Brisken

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsPhysicsPulsarScintillationInterstellar mediumAstrophysicsObservableSpectral lineCurvatureScatteringComputational physicsOpticsAstronomyGeometryQuantum mechanicsDetectorGalaxy

Abstract

fetched live from OpenAlex

ABSTRACT We introduce a novel analysis technique for pulsar secondary spectra. The power spectrum of pulsar scintillation (referred to as the ‘secondary spectrum’) shows differential delays and Doppler shifts due to interference from multipath propagation through the interstellar medium. We develop a transformation that maps these observables to angular coordinates on a single thin screen of phase-changing material. This transformation is possible without degeneracies in the case of a one-dimensional distribution of images on this screen, which is often a successful description of the phenomenon. The double parabolic features of secondary spectra are transformed into parallel linear features, whose properties we describe in detail. Furthermore, we introduce methods to measure the curvature parameter and the field amplitude distribution of images by applying them to observations of PSR B0834+06. Finally, we extend this formalism to two-dimensional distributions of images on the interstellar screen.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.364

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 head0.014
GPT teacher head0.194
Teacher spread0.180 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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