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
ACAT 2021 talk: optimising simulations for diphoton production at hadron colliders using amplitude neural networks
1 min read
physics
acat
·
acat2021
·
amplitudes
·
colliders
·
computational physics
·
conference
·
cross sections
·
differential cross sections
·
durham
·
hadron colliders
·
high energy physics
·
machine learning
·
neural networks
·
open source
·
particle physics
·
particle physics phenomenology
·
phd
·
precision qcd
·
presentation
·
qcd
·
quantum chromodynamics
·
quantum electrodynamics
·
quantum field theory
·
scattering amplitudes
·
standard model
·
talk
·
theoretical physics
·
virtual
Talk at 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research