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High-resolution calculations of merging neutron stars - II. Neutrino emission

2003· article· en· W1984649626 on OpenAlexaff
Stephan Rosswog, M. Liebendörfer

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsNeutrinoNeutron starSupernovaAstrophysicsCoalescence (physics)Nuclear physicsMeasurements of neutrino speedAnnihilationNeutrino oscillationSolar neutrinoAstronomy

Abstract

fetched live from OpenAlex

The remnant resulting from the merger of two neutron stars produces neutrinos in copious amounts. In this paper we present the neutrino emission results obtained via Newtonian, high-resolution simulations of the coalescence event. These simulations use three-dimensional smoothed particle hydrodynamics together with a nuclear, temperature-dependent equation of state and a multiflavour neutrino leakage scheme. We present the details of our scheme, discuss the neutrino emission results from a neutron star coalescence and compare them with the core-collapse supernova case where neutrino emission has been studied for several decades. The average neutrino energies are similar to those in the supernova case, but contrary to the latter, the luminosities are dominated by electron-type antineutrinos that are produced in the hot, neutron-rich, thick disc of the merger remnant. The cooler parts of this disc contain substantial fractions of heavy nuclei, which, however, do not influence the overall neutrino emission results significantly. Our total neutrino luminosities from the merger event are considerably lower than those found in previous investigations. This imposes constraints on the ability of neutron star mergers to produce a gamma-ray burst via neutrino annihilation. The neutrinos are emitted preferentially along the initial binary rotation axis, an event seen ‘pole-on’ would appear much brighter in neutrinos than a similar event seen ‘edge-on’.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.210
Teacher spread0.202 · 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 designObservational
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

Citations288
Published2003
Admission routes1
Has abstractyes

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