39 citations · 58 across the 6 of their papers we have counts for
12 papers · 1 filter
Lattice evidence that scalar glueballs are small
Ryan Abbott, Daniel C. Hackett, Dimitra A. Pefkou +2
This work reports the first calculation of the gravitational form factors (GFFs) of the scalar glueball, performed via lattice field theory in Yang-Mills theory at a single lattice…
Progress in Normalizing Flows for 4d Gauge Theories
Ryan Abbott, Denis Boyda, Daniel C. Hackett +4
Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensio…
Gravitational form factors of glueballs in Yang-Mills theory
Ryan Abbott, Daniel C. Hackett, Dimitra A. Pefkou +2
This work presents preliminary results of the first determination of the energy-momentum tensor form factors of the scalar glueball, referred to as gravitational form factors (GFFs…
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +10
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological…
Applications of Machine Learning to Lattice Quantum Field Theory
Denis Boyda, Salvatore Calì, Sam Foreman +8
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In th…
Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett +5
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The…