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Record W3095211402 · doi:10.1093/mnras/staa3428

Polarization of accreting X-ray pulsars. I. A new model

2020· article· en· W3095211402 on OpenAlexafffund
Ilaria Caiazzo, Jeremy Heyl

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of British Columbia
FundersCompute CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCalifornia Institute of Technology
KeywordsPhysicsPulsarPolarization (electrochemistry)AstrophysicsPolarimetryAstronomyGravitational waveAccretion (finance)Neutron starLight curveScatteringOptics

Abstract

fetched live from OpenAlex

ABSTRACT A new window is opening in high-energy astronomy: X-ray polarimetry. With many missions currently under development and scheduled to launch as early as 2021, observations of the X-ray polarization of accreting X-ray pulsars will soon be available. As polarization is particularly sensitive to the geometry of the emission region, the upcoming polarimeters will shed new light on the emission mechanism of these objects, provided that we have sound theoretical models that agree with current spectroscopic and timing observation and that can make predictions of the polarization parameters of the emission. We here present a new model for the polarized emission of accreting X-ray pulsars in the accretion column scenario that for the first time takes into account the macroscopic structure and dynamics of the accretion region and the propagation of the radiation towards the observer, including relativistic beaming, gravitational lensing, and quantum electrodynamics. In this paper, we present all the details of the model, while in a companion paper, we apply our model to predict the polarization parameters of the bright X-ray pulsar Hercules X-1.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.015
GPT teacher head0.212
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2020
Admission routes2
Has abstractyes

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