用K-近邻法深度回归μ子能量
Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
摘要：
在为未来粒子物理实验研究新的测量解决方案的背景下，我们开发了一个高性能的基于kNN的回归器来推断高度相对论性μ子的能量，从它们在密集和颗粒量热计中的辐射损失的模式。回归器基于一个弱kNN学习器池，这些学习器通过随机梯度下降来适应每个训练事件的权重和偏差。通过该过程优化的参数的有效数量在6000万范围内，因此与大型深度学习架构相当。我们通过将其与几种机器学习算法进行比较来测试回归器在所考虑的应用程序上的性能，显示出与提升决策树和神经网络所实现的精度相当的精度。
英文摘要：
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.
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