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Record W4322584509 · doi:10.1017/s1743921322000692

Probing Magnetars Using Spectral Lines with Future Telescopes

2020· article· en· W4322584509 on OpenAlexaff
Demet Kırmızıbayrak, Jeremy Heyl

Bibliographic record

VenueProceedings of the International Astronomical Union · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMagnetarPhysicsAstrophysicsMagnetic fieldContext (archaeology)Spectral lineEmission spectrumLine (geometry)Spectral resolutionAstronomyGeologyGeometry

Abstract

fetched live from OpenAlex

Abstract We present our findings on magnetar spectral line analysis in the context of upcoming high resolution, high effective area, high throughput X-ray telescopes for two cases: persistent magnetar emission and magnetar bursts. For magnetars in quiescence, we present our preliminary work on modelling for phase-resolved emission. Our results reveal the necessity of constraining line depth and width concurrently with line energy to conclusively determine hotspot emission and corresponding magnetic field geometry. We then present the results of our simulations using effective area and response of various current and upcoming X-ray telescopes for magnetar spectral line detection and expand on the exciting opportunities upcoming telescopes provide to probe quiescent and burst emission region geometry and propagation in the extreme magnetic field of a magnetar.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.261
Teacher spread0.248 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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