论文标题
使用K-Neart最邻居算法的MUON能量的深度回归
Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
论文作者
论文摘要
在用于对未来粒子物理实验的新型测量溶液的研究的背景下，我们开发了一种基于knn的回归剂，以从密集和颗粒状的热量计中从其辐射损失的模式中推断出高度偏见的muons的能量。回归器基于弱的KNN学习者，通过随机梯度下降使每个训练事件的体重和偏见调整来学习。该过程优化的有效数量参数在6000万范围内，因此与大型深度学习体系结构相当。我们通过将回归器与几种机器学习算法的表现进行比较，测试回归器在考虑应用程序上的性能，从而显示出与增强决策树和神经网络实现的相当精度。
Within the context of studies for novel measurement solutions for future particle physics experiments, we developed a performant kNN-based regressor to infer the energy of highly-relativistic muons from the pattern of their radiation losses in a dense and granular calorimeter. The regressor is based on a pool of weak kNN learners, which learn by adapting weights and biases to each training event through stochastic gradient descent. The effective number of parameters optimized by the procedure is in the 60 millions range, thus comparable to that of large deep learning architectures. We test the performance of the regressor on the considered application by comparing it to that of several machine learning algorithms, showing comparable accuracy to that achieved by boosted decision trees and neural networks.
