Using Deep Learning Algorithms to Improve Energy Resolution in the Semileptonic Decays

Abstract

The neutrino closure method can be used to obtain the decay kinematics with one missing final state particle (ν) in semileptonic decays. Its solution should give the square of the invariant mass of the lv system (q2) and momentum (P) of the decayed mother particle in semileptonic decay process. However, the resolution obtained by solving two-solution problems with existing algorithms is limited. We propose a new method based on deep learning to improve the resolution of the two key physical quantities when processing Large Hadron Collider beauty (LHCb) experimental data. Resolution of q2 (P) can be improved evenly 1.7% (8.2%) by regression algorithm and 2.7% (9.6%) by classification algorithm compared to linear regression algorithm. The resolution improvements using the new method will benefit the studies on semileptonic decays in hardon collider experiments. Moreover, the new method can be applied to other decays with a missing particle in the final state.