arXiv: optimising hadronic collider simulations using amplitude neural networks
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physics
acat
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acat2021
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amplitudes
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arxiv
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colliders
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computational physics
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conference
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contribution
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cross sections
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differential cross sections
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durham
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hadron colliders
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high energy physics
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inspirehep
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machine learning
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neural networks
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open source
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particle physics
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particle physics phenomenology
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phd
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precision qcd
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preprint
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proceedings
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qcd
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quantum chromodynamics
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quantum electrodynamics
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quantum field theory
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scattering amplitudes
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standard model
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theoretical physics
Preprint on arXiv for ACAT 2021 proceedings contribution
The preprint for my ACAT 2021 proceedings contribution, Optimising hadronic collider simulations using amplitude neural networks, appeared on arXiv today (InspireHEP). This follows my recent talk. It is also available through the GitLab CI system here (source).