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Record W4411748658 · doi:10.1093/mnras/staf992

General-relativistic lattice-Boltzmann method for radiation transport

2025· article· en· W4411748658 on OpenAlexaff
Tom Olsen, Luciano Rezzolla

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2025
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsTrinity College
FundersH2020 European Research CouncilWalter Greiner Gesellschaft
KeywordsPhysicsRadiation transportLattice Boltzmann methodsBoltzmann equationRadiationStatistical physicsAstrophysicsQuantum electrodynamicsClassical mechanicsComputational physicsQuantum mechanicsMonte Carlo method

Abstract

fetched live from OpenAlex

ABSTRACT We present the first extension of the special-relativistic lattice-Boltzmann method for radiative transport to solve the radiative-transfer equation in curved space–times. The novel approach is based on the streaming of carefully selected photons along null geodesics and interpolating their final positions, velocities, and frequency shifts to all photons in a given velocity stencil. Furthermore, by transforming between the laboratory frame, the Eulerian frame, and the fluid frame, we are able to perform the collision step in the fluid frame, thus retaining the collision operator of the special-relativistic case with only minor modifications. As a result, with the new method we can model the evolution of the frequency-independent (grey) radiation field as it interacts with a background fluid via absorption, emission, and scattering in a curved background space–time. Finally, by introducing a refined adaptive stencil, which is suitably distorted in the direction of propagation of the photon bundle, we can reduce the computational costs of the method while improving its performance in the optically thin regime. A number of standard and novel tests are presented to validate the approach and exhibit its robustness and accuracy.

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 score0.234
Threshold uncertainty score0.630

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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