论文标题
使用深度学习的校准
Per-Object Systematics using Deep-Learned Calibration
论文作者
论文摘要
我们展示了如何使用贝叶斯深网治疗系统不确定性进行回归。首先，我们分析了这些网络如何在增强的顶级夸克的动量上分别跟踪统计和系统的不确定性。接下来，我们通过在标签上进行培训及其误差线提出了一种新颖的校准程序。同样，网络可以干净地分开不同的不确定性。作为技术副作用，我们展示了如何扩展贝叶斯网络以描述非高斯特征。
We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertainties on the momenta of boosted top quarks forming fat jets. Next, we propose a novel calibration procedure by training on labels and their error bars. Again, the network cleanly separates the different uncertainties. As a technical side effect, we show how Bayesian networks can be extended to describe non-Gaussian features.
