Speaker
Description
Jets are key observables to measure the hadronic activities at high energy colliders such as the Large Hadron Collider (LHC) and future colliders such as the High Luminosity LHC (HL-LHC) and the Circular Electron Positron Collider (CEPC). Yet jet reconstruction is a computationally expensive task especially when the number of final-state particles is large. Such a clustering task can be regarded as an optimization problem, which can be formulated in terms of an Ising Hamiltonian and searching for its ground state would provide the answer. Quantum-annealing-inspired algorithms provide promising solutions to tackle the problem. This study opens up a new approach to globally reconstruct multijet beyond dijet in one-go, in contrast to the traditional iterative method.
