Machine learning fixed point actions with convolutional neural networks

A cura di Urs Wenger (University of Bern (Switzerland))

lunedì 5 feb 2024, 14:30 → 15:30 Europe/Rome

AULA PAOLUZI (https://infn-it.zoom.us/j/92432544823?pwd=djNCZldmOFRkUTlkNW5ZeFZ3WjRJQT09)

Fixed point lattice actions based on renormalization group transformations have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level. They provide a possible way to extract continuum physics with coarser lattices, thereby allowing to circumvent problems with critical slowing down and topological freezing toward the continuum limit. I describe how we use a gauge-equivariant convolutional neural network and machine learning methods to obtain a fixed point action for four-dimensional SU(3) gauge theory. The large operator space allows us to find superior parametrizations compared to previous studies, a necessary first step for future Monte Carlo simulations.