论文标题
根据机器学习
Improvement of $q^2$ resolution in semileptonic decays based on machine learning
论文作者
论文摘要
中微子闭合方法通常用于用一个未结构的粒子获得半衰减衰变的运动学。衰减的运动学可以通过具有二次方程式的两倍的歧义来扣除。为了解决两倍的歧义，提出了一种基于机器学习（ML）的新方法。我们研究了不同功能和回归器集合对改善$ \ ellν$ system〜（$ q^2 $）的重建不变质量平方的影响。结果表明，通过使用飞行向量作为特征获得最佳性能，而多层感知器（MLP）模型作为回归器。与随机选择相比，MLP模型将重建$ Q^2 $的分辨率提高到$ \ sim $ 40 \％。此外，显示了在各种半衰减衰减上使用这种方法的可能性。
The neutrino closure method is often used to obtain kinematics of semileptonic decays with one unreconstructed particle. The kinematics of decays can be deducted by a two-fold ambiguity with a quadratic equation. To resolve the two-fold ambiguity, a new 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 $\ell ν$ 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 $\sim$40\%. Furthermore, the possibility of using this method on various semileptonic decays is shown.
