论文标题
对抗结构域的适应性，以减少高能量物理分类器的样品偏差
Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
论文作者
论文摘要
我们在无监督的环境中应用对抗域的适应性，以减少受监督的高能量物理事件分类器培训中的样本偏差。我们利用包含事件的神经网络和带有梯度反转层的域分类器，一方面同时启用信号与背景事件分类，而另一方面，将网络响应到来自不同MC模型的背景样本的差异最小化，这些差异是通过对抗域分类损失源自不同的MC模型。我们以$ t \ bar {t} h $信号与$ t \ bar {t} b \ bar {b} $背景分类为$ t \ bar {
We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimising the difference in response of the network to background samples originating from different MC models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the LHC with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification and discuss implications and limitations of the method
