Improvement of q 2 IF 1.5 4区 物理与天体物理 Q3 PHYSICS, PARTICLES & FIELDS Advances in High Energy Physics Pub Date : 2023-03-27 DOI:10.1155/2023/8127604 Panting Ge, Xiaotao Huang, M. Saur, Liang Sun

Abstract

The neutrino closure method is often used to obtain kinematics of semileptonic decays with one unreconstructed particle in hadron collider experiments. The kinematics of decays can be deducted by a twofold ambiguity with a quadratic equation. To resolve the twofold ambiguity, a novel method based on machine learning (ML) is proposed. We study the effect of different sets of features and regressors on the improvement of reconstructed invariant mass squared of ℓ ν system ( q 2 ). The result shows that the best performance is obtained by using the flight vector as the features and the multilayer perceptron (MLP) model as the regressor. Compared with the random choice, the MLP model improves the resolution of reconstructed q 2 by ~40%. Furthermore, the possibility of using this method on various semileptonic decays is shown.