4 papers
Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window
D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk +3
We study the machine learning techniques applied to the lattice gauge theory's critical behavior, particularly to the confinement/deconfinement phase transition in the SU(2) and SU…
Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda +5
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this fram…
New way of collision experiment data analysis based on Grand Canonical Distribution and Lattice QCD data
V. Bornyakov, D. Boyda, V. Goy +2
We propose new way of heavy ion collisions experiment data analysis. We analyze physical parameters of fireball created in RHIC experiment based on Grand Canonical Distribution and…
Lee-Yang zeros in lattice QCD for searching phase transition points
M. Wakayama, V. G. Bornyakov, D. L. Boyda +5
We report Lee-Yang zeros behavior at finite temperature and density. The quark number densities, <n>, are calculated at the pure imaginary chemical potential, where no sign problem…