75 citations · 460 across the 20 of their papers we have counts for
20 papers
Variance reduction in lattice QCD observables via normalizing flows
Ryan Abbott, Denis Boyda, Yang Fu +5
Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parame…
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…
Topological data analysis of the deconfinement transition in SU(3) lattice gauge theory
Daniel Spitz, Julian M. Urban, Jan M. Pawlowski
We study the confining and deconfining phases of pure lattice gauge theory with topological data analysis. This provides unique insights into long range correlatio…
Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda +5
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 increasin…
Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda +5
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…
Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda +7
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 act…
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
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 repo…
Non-perturbative strong coupling at timelike momenta
Jan Horak, Jan M. Pawlowski, Jonas Turnwald +3
We compute the strong coupling constant of Landau gauge QCD in the full complex momentum plane, both directly and via spectral reconstruction. In particular, we consider the Taylor…
Yang-Mills glueball masses from spectral reconstruction
Jan M. Pawlowski, Coralie S. Schneider, Jonas Turnwald +2
We compute masses of the two lightest glueballs from spectral reconstructions of timelike interaction channels of the four-gluon vertex in Landau gauge Yang-Mills theory. The Eucli…
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…
Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
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 gaug…
Confinement in non-Abelian lattice gauge theory via persistent homology
Daniel Spitz, Julian M. Urban, Jan M. Pawlowski
We investigate the structure of confining and deconfining phases in SU(2) lattice gauge theory via persistent homology, which gives us access to the topology of a hierarchy of comb…
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda +9
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic deter…
Flow-based density of states for complex actions
Jan M. Pawlowski, Julian M. Urban
Emerging sampling algorithms based on normalizing flows have the potential to solve ergodicity problems in lattice calculations. Furthermore, it has been noted that flows can be us…
Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer +7
Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodyna…
Reconstructing QCD Spectral Functions with Gaussian Processes
Jan Horak, Jan M. Pawlowski, José Rodríguez-Quintero +4
We reconstruct ghost and gluon spectral functions in 2+1 flavor QCD with Gaussian process regression. This framework allows us to largely suppress spurious oscillations and other c…
Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière +6
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotical…
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
Stefan Bluecher, Lukas Kades, Jan M. Pawlowski +2
Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples. Available…
Spectral Reconstruction with Deep Neural Networks
Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf +5
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem. Our a…
Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks
Jan M. Pawlowski, Julian M. Urban
Short autocorrelation times are essential for a reliable error assessment in Monte Carlo simulations of lattice systems. In many interesting scenarios, the decay of autocorrelation…