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Record W2798013443 · doi:10.3847/1538-4357/aabe92

Growth of Beam–Plasma Instabilities in the Presence of Background Inhomogeneity

2018· article· en· W2798013443 on OpenAlexaff
Mohamad Shalaby, Avery E. Broderick, Philip Chang, Christoph Pfrommer, A. Lamberts, Ewald Puchwein

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsInstabilityPlasmaPhysicsBeam (structure)Two-stream instabilitySaturation (graph theory)Growth rateAtomic physicsHomogeneousAstrophysicsOpticsMechanicsNuclear physicsGeometryStatistical physics

Abstract

fetched live from OpenAlex

Abstract We explore how inhomogeneity in the background plasma number density alters the growth of electrostatic unstable wavemodes of beam–plasma systems. This is particularly interesting for blazar-driven beam–plasma instabilities, which may be suppressed by inhomogeneities in the intergalactic medium (IGM) as was recently claimed in the literature. Using high-resolution particle-in-cell simulations with the SHARP code, we show that the growth of the instability is local, i.e., regions with almost homogeneous background density will support the growth of the Langmuir waves even when they are separated by strongly inhomogeneous regions, resulting in an overall slower growth of the instability. We also show that if the background density is continuously varying, the growth rate of the instability is lower, although in all cases the system remains within the linear regime longer and the instability is not extinguished. In all cases, the beam loses approximately the same fraction of its initial kinetic energy in comparison to the uniform case at nonlinear saturation. Thus, inhomogeneities in the IGM are unlikely to suppress the growth of blazar-driven beam–plasma instabilities.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.340

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 designTheoretical or conceptual
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

Citations29
Published2018
Admission routes1
Has abstractyes

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