no code implementations • 17 Apr 2024 • Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes.

no code implementations • 19 Jan 2024 • Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

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.

no code implementations • 3 May 2023 • Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

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.

no code implementations • 14 Nov 2022 • Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

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.

no code implementations • 18 Jul 2022 • Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.

no code implementations • 23 Feb 2022 • Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.

no code implementations • 25 Jan 2021 • Maxwell T. Hansen, Fernando Romero-López, Stephen R. Sharpe

We derive relations between finite-volume matrix elements and infinite-volume decay amplitudes, for processes with three spinless, degenerate and either identical or non-identical particles in the final state.

High Energy Physics - Lattice High Energy Physics - Phenomenology

no code implementations • 6 Dec 2020 • Pilar Hernández, Fernando Romero-López

We review recent progress in the study of the large $N_c$ limit of gauge theories from lattice simulations.

High Energy Physics - Lattice High Energy Physics - Phenomenology

1 code implementation • 7 Aug 2020 • Matthias Fischer, Bartosz Kostrzewa, Liuming Liu, Fernando Romero-López, Martin Ueding, Carsten Urbach

In the two-particle sector, we observe good agreement to the phenomenological fits in $s$- and $d$-wave, and obtain $M_\pi a_0 = -0. 0481(86)$ at the physical point from a direct computation.

High Energy Physics - Lattice High Energy Physics - Phenomenology

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