The Inverse Renormalisation Group in Quantum Field Theories

by Prof. Biagio Lucini (Swansea University)

Monday 13 Jun 2022, 13:00 → 14:00 Europe/Berlin

Recently, machine learning methods have found various applications in Statistical Physics, Condensed Matter and Quantum Field Theory. In this talk, after reviewing recent progress on investigations of phase transitions using machine learning methods, I propose inverse renormalisation group transformations within the context of quantum field theory that produce the appropriate critical fixed point structure, give rise to inverse flows in parameter space, and evade the critical slowing down effect in calculations pertinent to criticality. Given configurations of the two-dimensional $ϕ^4$ scalar field theory on sizes as small as $V = 8^2$, we apply the inverse transformations to produce rescaled systems of size up to $V=512^2$ which we utilise to extract two critical exponents. We conclude by discussing how the approach is generally applicable to any method that successfully produces configurations from a statistical ensemble and how it can give novel insights into the structure of the renormalisation group.

IRG_DESY_IND.pdf