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Record W6948355310 · doi:10.48550/arxiv.2107.10858

Modeling Fast Radio Burst Dispersion and Scattering Properties in the First CHIME/FRB Catalog

2021· preprint· en· W6948355310 on OpenAlexaboutno aff

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsScatteringMilky WayPopulationGalaxyPulsarInterstellar mediumHalo

Abstract

fetched live from OpenAlex

We present a Monte Carlo-based population synthesis study of fast radio burst (FRB) dispersion and scattering focusing on the first catalog of sources detected with the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) project. We simulate intrinsic properties and propagation effects for a variety of FRB population models and compare the simulated distributions of dispersion measures (DMs) and scattering timescales with the corresponding distributions from the CHIME/FRB catalog. Our simulations confirm the results of previous population studies, which suggested that the interstellar medium of the host galaxy alone (simulated based on the NE2001 model) cannot explain the observed scattering timescales of FRBs. We therefore consider additional sources of scattering, namely, the circumgalactic medium (CGM) of intervening galaxies and the circumburst medium whose properties are modeled based on typical Galactic plane environments. We find that a population of FRBs with scattering contributed by these media is marginally consistent with the CHIME/FRB catalog. In this scenario, our simulations favor a population of FRBs offset from their galaxy centers over a population which is distributed along the spiral arms. However, if the models proposing the CGM as a source of intense scattering are incorrect, then we conclude that FRBs may inhabit environments with more extreme properties than those inferred for pulsars in the Milky Way.

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 score0.277
Threshold uncertainty score0.987

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.092
GPT teacher head0.163
Teacher spread0.071 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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