Speaker
Thomas Spriggs
(Delft University of Technology)
Description
The study and impact of lattice gauge theories on high-energy physics cannot be understated. However, the difficulties involved in simulating strongly-coupled systems has hampered our attempts to fully understand phenomena like quark confinement and hadronisation. We present an application of state-of-the-art machine learning techniques under the umbrella of neural quantum states to high-energy physics. Notably, the ansatze for this work combines physically motivated Jastrow terms with non-trivial equivariance under gauge transformations. Using this, we will present accurate determinations of ground state properties of the non-Abelian SU(2) lattice field theory across a range of inverse couplings.
Email Address of submitter
t.e.spriggs@tudelft.nl
Author
Thomas Spriggs
(Delft University of Technology)
Co-authors
Dr
Eliska Greplova
(Delft University of Technology)
Dr
Jannnes Nys
(ETH, Zurich)
