Professor Michael Peardon

  ALGORITHMS   CHARMED PARTICLES   GLUEBALL SPECTRUM   MARKOV CHAIN MONTE CARLO   MONTE CARLO METHODS   MONTE-CARLO ALGORITHMS   PARTICLE PHYSICS   Physics   QUANTUM CHROMODYNAMICS   QUARK   QUARK CONFINEMENT

My research explores strongly interacting quantum field theories such as quantum chromodynamics (QCD), the theory of the strong nuclear force which binds quarks inside the proton and neutron. I am particularly interested in developing new numerical techniques for ab initio calculations starting from the foundations of quantum field theory and using a four-dimensional space-time lattice to represent the quantum vacuum through computer simulation. Currently, I am addressing the question of whether particles called glueballs and made predominantly from gluons, the force-carriers of QCD can be observed in collider experiments if they are not too short-lived to be detected. The large-scale computations needed to investigate this physics are performed using Monte Carlo methods on high-performance computer systems. Monte Carlo uses statistical sampling to estimate predictions from the theory and many of these calculations are too imprecise in statistical resolution. My current work therefore focuses on developing better mathematical frameworks to perform these computations which give a much better signal-to-noise while simultaneously developing deeper physical insights to help optimise the computations. To achieve these aims requires me to connect the mathematics of random processes with insight into particle physics phenomenology and high-performance computing techniques. A technique I pioneered, termed distillation is an example of this synthesis which has proved to be a highly effective framework, now increasingly widely used for many state-of-the-art calculations in lattice QCD spectroscopy over the past decade. Currently, I am developing techniques to split large computations into smaller blocks each of which can be evaluated independently to allow the full computation to be performed far more efficiently once components are reassembled. This work is developing as part of a new collaboration with researchers in Germany, significantly funded by the DFG (German Research Funding Agency), which has appointed me as a Mercator Fellow to support this collaboration.