论文标题
使用机器学习在高能量物理学中处理滋扰参数：评论
Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review
论文作者
论文摘要
在这项工作中，我们讨论了滋扰参数对机器学习在高能物理问题中的有效性的影响，并提供了对技术的综述，这些技术允许包括其效果并减少其在寻找最佳选择标准和可变转换时的影响。滋扰参数的引入使监督的学习任务及其对应关系与数据分析目标复杂化，因为它们的贡献使模型性能降低了真实数据中的模型性能，以及在所得的统计推断中必要添加不确定性。讨论的方法包括滋扰参数化的模型，修改或对手损失，半监督学习方法以及推理意识的技术。
In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to include their effect and reduce their impact in the search for optimal selection criteria and variable transformations. The introduction of nuisance parameters complicates the supervised learning task and its correspondence with the data analysis goal, due to their contribution degrading the model performances in real data, and the necessary addition of uncertainties in the resulting statistical inference. The approaches discussed include nuisance-parameterized models, modified or adversary losses, semi-supervised learning approaches, and inference-aware techniques.
