This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement Nยบ 847635.
Department of Department of Theoretical Particle Physics
Institute of Theoretical Physics, Faculty of Physics, Astronomy and Applied Computer Physics
The Department of Theoretical Particle Physics is composed of 4 senior staff members, myself (associate professor), one postdoc and 3 Master/Bachelor students. We have strong collaboration ties with many groups at the international level (Institute for Nuclear Theory University of Washington in Seattle, Tor Vergata University of Rome, Humboldt University in Berlin, University of Regensburg). We have access to computing resources at the major polish supercomputing center as well as to the world-leading supercomputing infrastructure at the European level (for example superMUC-NG in Garching).
We are developing numerical Monte Carlo simulations of Quantum Chromodynamics, the theory describing the interactions of quarks and gluons. Using the largest supercomputers in the world we are able to provide fully non-perturbative determinations of hadronic matrix elements which enter in theoretical predictions of decay rates based on the Standard Model. Numerical estimates of decay rates can be then compared with experimental measurements from experiments conducted at CERN, BNL, Fermilab and elsewhere, allowing for a precise determination of the Standard Model parameters, and also providing opportunities for searches of physics beyond the Standard Model. We are interested in several quantities such as the strong coupling constant, non-perturbative condensates, hadron structure functions (in particular the Transverse Momentum Dependent structure functions). We contributing not only to the physics program of Monte Carlo simulations. We are developing new implementations of algorithms taking advantage of the newest computer architectures, such as new FPGA devices like Alveo cards from Xilinx. We are also investigating the applicability of machine learning techniques to Monte Carlo simulations.