Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Abbott, Ryan P."'
Autor:
Abbott, Ryan, Hackett, Daniel C., Pefkou, Dimitra A., Romero-López, Fernando, Shanahan, Phiala
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). The calculation has been carried out in lattice Yang-Mills theory a
Externí odkaz:
http://arxiv.org/abs/2410.02706
Autor:
Abbott, Ryan, Detmold, William, Illa, Marc, Parreño, Assumpta, Perry, Robert J., Romero-López, Fernando, Shanahan, Phiala E., Wagman, Michael L.
Understanding the behavior of dense hadronic matter is a central goal in nuclear physics as it governs the nature and dynamics of astrophysical objects such as supernovae and neutron stars. Because of the non-perturbative nature of quantum chromodyna
Externí odkaz:
http://arxiv.org/abs/2406.09273
Autor:
Abbott, Ryan, Albergo, Michael S., Boyda, Denis, Hackett, Daniel C., Kanwar, Gurtej, Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question
Externí odkaz:
http://arxiv.org/abs/2404.11674
Autor:
Abbott, Ryan, Albergo, Michael S., Boyda, Denis, Hackett, Daniel C., Kanwar, Gurtej, Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field theories, but
Externí odkaz:
http://arxiv.org/abs/2404.10819
Autor:
Abbott, Ryan, Botev, Aleksandar, Boyda, Denis, Hackett, Daniel C., Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo J., Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be e
Externí odkaz:
http://arxiv.org/abs/2401.10874
Autor:
Abbott, Ryan, Detmold, William, Romero-López, Fernando, Davoudi, Zohreh, Illa, Marc, Parreño, Assumpta, Perry, Robert J., Shanahan, Phiala E., Wagman, Michael L.
We present an algorithm to compute correlation functions for systems with the quantum numbers of many identical mesons from lattice quantum chromodynamics (QCD). The algorithm is numerically stable and allows for the computation of $n$-pion correlati
Externí odkaz:
http://arxiv.org/abs/2307.15014
Autor:
Abbott, Ryan, Albergo, Michael S., Botev, Aleksandar, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Kanwar, Gurtej, Matthews, Alexander G. D. G., Racanière, Sébastien, Razavi, Ali, Rezende, Danilo J., Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We report new algorithmic developments of gauge-equivariant flow architecture
Externí odkaz:
http://arxiv.org/abs/2305.02402
Autor:
Abbott, Ryan, Albergo, Michael S., Botev, Aleksandar, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Matthews, Alexander G. D. G., Racanière, Sébastien, Razavi, Ali, Rezende, Danilo J., Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
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 freezing. However, these demonstrations have been at the scale of toy
Externí odkaz:
http://arxiv.org/abs/2211.07541
Autor:
Abbott, Ryan, Albergo, Michael S., Botev, Aleksandar, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Kanwar, Gurtej, Matthews, Alexander G. D. G., Racanière, Sébastien, Razavi, Ali, Rezende, Danilo J., Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories. A critical qu
Externí odkaz:
http://arxiv.org/abs/2208.03832
Autor:
Abbott, Ryan, Albergo, Michael S., Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo J., Romero-López, Fernando, Shanahan, Phiala E., Tian, Betsy, Urban, Julian M.
Publikováno v:
Phys.Rev.D 106 (2022) 7, 074506
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art lattice field
Externí odkaz:
http://arxiv.org/abs/2207.08945