论文标题
通过非参数方法有效地学习新物理学
Learning new physics efficiently with nonparametric methods
论文作者
论文摘要
我们为独立于模型的新物理搜索提供了一种机器学习方法。相应的算法由最新的内核方法实现，非参数学习算法的实现提供动力，这些算法可以在给定足够数据的情况下近似任何连续功能。基于D'Agnolo和Wulzer（Arxiv：1806.02350）的原始建议，该模型通过基于可能性比率实施假设测试程序来评估实验数据和参考模型之间的兼容性。通过避免对测量中新物理成分的存在或形状的任何先前假设来实现模型独立性。我们表明，与神经网络实施相比，在培训时间和计算资源方面，我们的方法具有巨大的优势，同时保持了可比的性能。特别是，我们对更高维数据集进行了测试，这是对以前的研究的向前迈出的一步。
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.
