MACHINE LEARNING AND ALGORITHMS
Machine learning techniques in particle physics
The classification of charged particle jets according to their pattern they leave in the calorimetric cells of the ALICE (CERN) detector can be approached by specifically tailored convolutional neural network if one treats traces of the jets as images. One particularly important target of our jet ML models is the signal-to-background ratio which is learned using experimental data obtained from known jets embedded in background. Boosted Decision Trees is an appropriate shallow ML approach for particle identification and it is used in models often trained using realistic Monte Carlo simulations.
Machine learning in statistical and quantum physics
Our interests address learning algorithms which we use to train physically relevant models of quantum systems such as Boltzmann machines and yield new classes of meaningful solutions of ensembles of interacting particles. Quantum machine learning algorithms involving reinforcement learning techniques help us extract prominent and very perspective testbed for future applications of quantum computers in optimizing objective functions of many-body systems.